Distributed data set indexing

ABSTRACT

An apparatus including a processor to receive search criteria including a data value for a search within a data field; in response to the receipt of the query instructions, and for each data cell within a super cell, perform the specified search by comparing the data value to ranges of values indicated in a corresponding cell index to determine whether the data cell includes a data record meeting the search criteria, and in response to a determination that the data cell includes such a data record, use a unique values index in the cell index to search the data records of the data cell to identify one or more data records meeting the search criteria; and in response to identifying at least one data record meeting the search criteria, provide an indication that at least the data cell includes at least one data record meeting the search criteria.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation under the provisions of 35 U.S.C. §120 of U.S. application Ser. No. 15/838,110 filed Dec. 11, 2017, whichis incorporated herein by reference in its entirety for all purposes.This application also claims the benefit of priority under 35 U.S.C. §119(e) to U.S. Provisional Application Ser. No. 62/458,162 filed Feb.13, 2017, which is also incorporated herein by reference in its entiretyfor all purposes.

TECHNICAL FIELD

Various embodiments described herein are generally directed tointer-device coordination to improve distributed indexing of a data setstored by multiple node devices.

BACKGROUND

The performance of analyses of large data sets (e.g., what is commonlyreferred to as “big data”) is becoming increasingly commonplace in suchareas as simulations, process monitoring, decision making, behavioralmodeling and making predictions. Such analysis are often performed bygrids of varying quantities of available node devices, while the datasets are often stored within a separate set of storage devices. Thisbegets the challenge of enabling efficiently generating indexes for suchlarge data sets to enable efficient searching of such large data setsacross multiple node devices of a grid to enable specific pieces of datato be efficiently located and retrieved.

SUMMARY

This summary is not intended to identify only key or essential featuresof the described subject matter, nor is it intended to be used inisolation to determine the scope of the described subject matter. Thesubject matter should be understood by reference to appropriate portionsof the entire specification of this patent, any or all drawings, andeach claim.

An apparatus includes a processor of a first node device of multiplenode devices, and a storage of the first node device to storeinstructions that, when executed by the processor, cause the processorto perform operations including, receive, at the first node device, asuper cell of multiple super cells into which a data set is divided froma data file maintained by at least one data device, wherein: themultiple super cells are distributed among the multiple node devices,each super cell includes multiple data cells, each data cell of themultiple data cells includes multiple data records, and each data recordof the multiple data records includes a set of fields at which datavalues of the data set are stored. The processor may also be caused toindex, at the first node device, and at least partially in parallel withother node devices of the multiple node devices, the multiple datarecords within each data cell of the multiple data cells by a first datafield and by a second data field of the set of fields in a single readpass through each data cell of the multiple data cells, wherein for eachdata record within a first data cell of the received super cell, theprocessor is caused to: retrieve a data value from the first data fieldand a data value from the second data field; determine, based on thedata value retrieved from the first data field, whether the first datafield of the data record stores a unique data value, wherein the datavalue has not yet been retrieved by the processor from the first datafield of any data record of the first data cell; in response to adetermination that the first data field of the data record stores aunique data value, add an identifier of the data record to a firstunique values index of a first cell index corresponding to the firstdata cell, wherein identifiers of data records within the first uniquevalues index are ordered based on the corresponding unique data valuesin the first data field to enable use of the first unique values indexto perform a search of the data values within the first data field ofthe data records of the first data cell; determine, based on the datavalue retrieved from the second data field, whether the second datafield of the data record stores a unique data value, wherein the datavalue has not yet retrieved by the processor from the second data fieldof any data record of the first data cell; and in response to adetermination that the second data field of the data record stores aunique data value, add an identifier of the data record to a secondunique values index of the first cell index, wherein identifiers of datarecords within the second unique values index are ordered based on thecorresponding unique data values in the second data field to enable useof the second unique values index to perform a search of the data valueswithin the second data field of the data records of the first data cell.The processor may further be caused to generate, within a super cellindex corresponding to the received super cell, an indication of a rangeof the data values of the first data field within the data records ofthe first data cell, and an indication of a range of the data values ofthe second data field within the data records of the first data cell, toenable use of the super cell index to determine whether a valuespecified in search criteria is present within one of the first andsecond data fields of any data record of the first data cell; provide,to a control device, a request for a first pointer to a location withinthe data file at which to store the super cell, the super cell index andthe first cell index; receive, at the first node device and from thecontrol device, the first pointer; and transmit, to the at least onedata device and at least partially in parallel with other node devicesof the multiple node devices, the super cell, the super cell index andthe first cell index with an instruction to store the super cell, thesuper cell index and the first cell index with the super cell stored inthe data file starting at the location pointed to by the first pointer,with the super cell index and the first cell index stored in the datafile at a location after the super cell.

A computer-program product tangibly embodied in a non-transitorymachine-readable storage medium includes instructions operable to causea processor of a first node device of multiple node devices to performoperations including, receive, at the first node device, a super cell ofmultiple super cells into which a data set is divided from a data filemaintained by at least one data device, wherein: the multiple supercells are distributed among the multiple node devices, each super cellincludes multiple data cells, each data cell of the multiple data cellsincludes multiple data records, and each data record of the multipledata records includes a set of fields at which data values of the dataset are stored. The processor may also be caused to index, at the firstnode device, and at least partially in parallel with other node devicesof the multiple node devices, the multiple data records within each datacell of the multiple data cells by a first data field and by a seconddata field of the set of fields in a single read pass through each datacell of the multiple data cells, wherein for each data record within afirst data cell of the received super cell, the processor is caused to:retrieve a data value from the first data field and a data value fromthe second data field; determine, based on the data value retrieved fromthe first data field, whether the first data field of the data recordstores a unique data value, wherein the data value has not yet beenretrieved by the processor from the first data field of any data recordof the first data cell; in response to a determination that the firstdata field of the data record stores a unique data value, add anidentifier of the data record to a first unique values index of a firstcell index corresponding to the first data cell, wherein identifiers ofdata records within the first unique values index are ordered based onthe corresponding unique data values in the first data field to enableuse of the first unique values index to perform a search of the datavalues within the first data field of the data records of the first datacell; determine, based on the data value retrieved from the second datafield, whether the second data field of the data record stores a uniquedata value, wherein the data value has not yet retrieved by theprocessor from the second data field of any data record of the firstdata cell; and in response to a determination that the second data fieldof the data record stores a unique data value, add an identifier of thedata record to a second unique values index of the first cell index,wherein identifiers of data records within the second unique valuesindex are ordered based on the corresponding unique data values in thesecond data field to enable use of the second unique values index toperform a search of the data values within the second data field of thedata records of the first data cell. The processor may further be causedto generate, within a super cell index corresponding to the receivedsuper cell, an indication of a range of the data values of the firstdata field within the data records of the first data cell, and anindication of a range of the data values of the second data field withinthe data records of the first data cell, to enable use of the super cellindex to determine whether a value specified in search criteria ispresent within one of the first and second data fields of any datarecord of the first data cell; provide, to a control device, a requestfor a first pointer to a location within the data file at which to storethe super cell, the super cell index and the first cell index; receive,at the first node device and from the control device, the first pointer;and transmit, to the at least one data device and at least partially inparallel with other node devices of the multiple node devices, the supercell, the super cell index and the first cell index with an instructionto store the super cell, the super cell index and the first cell indexwith the super cell stored in the data file starting at the locationpointed to by the first pointer, with the super cell index and the firstcell index stored in the data file at a location after the super cell.

The received super cell may include a second data cell in addition tothe first data cell; the processor may be caused to index the multipledata records within the second data cell by the first data field and bythe second data field of the set of fields in a single read pass togenerate additional unique values indexes within the first cell index toenable a binary search of the data values within at least one of thefirst data field and the second data field of the data records withinthe second data cell; a first processor core of the processor may indexthe multiple data records within the first data cell; and a secondprocessor core of the processor may index the multiple data recordswithin the second data cell at least partially in parallel with theindexing, by the first processor core, of the multiple data recordswithin the first data cell.

The processor may be caused to: for each data cell within the supercell, add a highest data value and a lowest data value of the range ofdata values of the first data field to a third binary tree; perform anin-order traversal of the third binary tree to identify highest andlowest data values of the first data field among the data cells withinthe super cell; add indications of the highest and lowest values of thefirst data field among the data cells within the super cell to the supercell index to specify a range of values of the first data field for thesuper cell therein; for each data cell within the super cell, add ahighest data value and a lowest data value of the range of data valuesof the second data field to a fourth binary tree; perform an in-ordertraversal of the fourth binary tree to identify highest and lowest datavalues of the second data field among the data cells within the supercell; and add indications of the highest and lowest values of the seconddata field among the data cells within the super cell to the super cellindex to specify a range of values of the second data field for thesuper cell therein.

Following each retrieval of data values from the first data field andthe second data field of a data record of the multiple data records, theprocessor may be caused to perform operations including: search a firstbinary tree of unique data values of the first data field of the firstdata cell correlated to the identifier of a data record to determinewhether the data value retrieved from the first data field includes aduplicate data value that is already present within the first binarytree; add the data value retrieved from the first data field to thefirst binary tree in response to a determination that the data valueretrieved from the first data field is a unique data value that is notalready present within the first binary tree; search a second binarytree of unique data values of the second data field of the first datacell correlated to the identifier of a data record to determine whetherthe data value retrieved from the second data field includes a duplicatedata value that is already present within the second binary tree; andadd the data value retrieved from the second data field to the secondbinary tree in response to a determination that the data value retrievedfrom the second data field is a unique data value that is not alreadypresent within the second binary tree. The processor may also be causedto perform operations including generate the first unique values indexbased on an in-order traversal of the first binary tree, and generatethe second unique values index based on an in-order traversal of thesecond binary tree.

Following each retrieval of a data value from the first data field of adata record of the multiple data records, the processor may be caused toperform operations including: in response to a determination that thefirst data field of the data record stores a duplicate data value,search a first set of duplicate value indexes within the first cellindex to determine whether a duplicate value index already exists withinthe first set for the duplicate value; in response to identifying anexisting duplicate value index for the duplicate value within the firstset, add the identifier of the data record to the identified existingduplicate value index; and in response to determining that there is noexisting duplicate value index among the first set of duplicate valueindexes for the duplicate value, add a duplicate value index to thefirst set for the duplicate value and add the identifier of the datarecord to the added duplicate value index. Following each retrieval of adata value from the second data field of a data record of the multipledata records, the processor may be caused to perform operationsincluding: in response to a determination that the second data field ofthe data record stores a duplicate data value, search a second set ofduplicate value indexes with the first cell index to determine whether aduplicate value index already exists within the second set for theduplicate value; in response to identifying an existing duplicate valueindex for the duplicate value within the second set, add the identifierof the data record to the identified existing duplicate value index; andin response to determining that there is no existing duplicate valueindex among the second set of duplicate value indexes for the duplicatevalue, add a duplicate value index to the second set for the duplicatevalue and add the identifier of the data record to the added duplicatevalue index. Each duplicate value index within the first and second setsof duplicate value indexes may include identifiers of data records.

The processor may be caused to transmit an indication of currentavailability of resources of the first node device to the controldevice, at least partially in parallel with transmissions by other nodedevices of the multiple node devices of availability of resources to thecontrol device, to enable the control device to determine whether toassign the super cell to the first node device in lieu of assigning thesuper cell to another node device of the multiple node devices. Theprocessor may also be caused to perform operations including receive, atthe first node device and from the control device, a second pointer to alocation within data file from which to retrieve the super cell; andtransmit, to the at least on data device and at least partially inparallel with other node devices of the multiple node devices, aninstruction to provide the super cell to the first node device.

The processor may be caused to perform operations including: receive, atthe first node device, and at least partially in parallel with othernode devices of the multiple node devices, query instructions specifyingthe search criteria of a search to be performed of the data set for datarecords meeting the search criteria, wherein the search criteriaincludes at least one data value to be searched for within at least oneof the first data field and the second data field; compare the at leastone data value to at least one of the range of values of the first datafield and the range of values of the second data field specified by thesuper cell index to determine whether the super cell includes any datarecords that meet the search criteria; and in response to adetermination that at least one data record within at least one datacell of the super cell does include a data record that meets the searchcriteria, compare the at least one data value to at least one of therange of values of the first data field and the range of values of thesecond data field specified by the super cell index to determine atleast whether the first data cell includes any data records that meetthe search criteria.

The processor may be caused to perform operations including: in responseto a determination that at least the first data cell does include a datarecord that meets the search criteria, use at least one of the firstunique values index and the second values index to perform a binarysearch of the data records of the first data cell to identify one ormore data records of the first data cell that meet the search criteria;and upon identifying a data record of the first data cell that meets thesearch criteria, search at least one of the first set of duplicate valueindexes and the second set of duplicate value indexes for a duplicatevalue index that identifies one or more additional data records of thefirst data cell that meet the search criteria.

The processor is caused to perform operations including: parse the queryinstructions to determine whether the query instructions include taskinstructions for the performance of a task with data retrieved from oneor more data records identified as meeting the search criteria; and inresponse to a determination that the query instructions do include taskinstructions for the performance of a task, execute the instructions toperform the task at least partially in parallel with at least one othernode device of the multiple node devices.

A computer-implemented method includes receiving, at a first node deviceof multiple node devices, a super cell of multiple super cells intowhich a data set is divided from a data file maintained by at least onedata device, wherein: the multiple super cells are distributed among themultiple node devices; each super cell includes multiple data cells;each data cell of the multiple data cells includes multiple datarecords; and each data record of the multiple data records includes aset of fields at which data values of the data set are stored. Themethod may also include indexing, at the first node device, and at leastpartially in parallel with other node devices of the multiple nodedevices, the multiple data records within each data cell of the multipledata cells by a first data field and by a second data field of the setof fields in a single read pass through each data cell of the multipledata cells, wherein for each data record within a first data cell of thereceived super cell, the operations including: retrieving a data valuefrom the first data field and a data value from the second data field;determining, based on the data value retrieved from the first datafield, whether the first data field of the data record stores a uniquedata value, wherein the data value has not yet been retrieved from thefirst data field of any data record of the first data cell; in responseto a determination that the first data field of the data record stores aunique data value, adding an identifier of the data record to a firstunique values index of a first cell index corresponding to the firstdata cell, wherein identifiers of data records within the first uniquevalues index are ordered based on the corresponding unique data valuesin the first data field to enable use of the first unique values indexto perform a search of the data values within the first data field ofthe data records of the first data cell; determining, based on the datavalue retrieved from the second data field, whether the second datafield of the data record stores a unique data value, wherein the datavalue has not yet retrieved from the second data field of any datarecord of the first data cell; and in response to a determination thatthe second data field of the data record stores a unique data value,adding an identifier of the data record to a second unique values indexof the first cell index, wherein identifiers of data records within thesecond unique values index are ordered based on the corresponding uniquedata values in the second data field to enable use of the second uniquevalues index to perform a search of the data values within the seconddata field of the data records of the first data cell;

The method may further include: generating, within a super cell indexcorresponding to the received super cell, an indication of a range ofthe data values of the first data field within the data records of thefirst data cell, and an indication of a range of the data values of thesecond data field within the data records of the first data cell, toenable use of the super cell index to determine whether a valuespecified in search criteria is present within one of the first andsecond data fields of any data record of the first data cell; providing,to a control device, a request for a first pointer to a location withinthe data file at which to store the super cell, the super cell index andthe first cell index; receiving, at the first node device and from thecontrol device, the first pointer; and transmitting, to the at least onedata device and at least partially in parallel with other node devicesof the multiple node devices, the super cell, the super cell index andthe first cell index with an instruction to store the super cell, thesuper cell index and the first cell index with the super cell stored inthe data file starting at the location pointed to by the first pointer,with the super cell index and the first cell index stored in the datafile at a location after the super cell.

The received super cell may include a second data cell in addition tothe first data cell; the method may include indexing the multiple datarecords within the second data cell by the first data field and by thesecond data field of the set of fields in a single read pass to generateadditional unique values indexes within the first cell index to enable abinary search of the data values within at least one of the first datafield and the second data field of the data records within the seconddata cell; a first processor core of the processor may index themultiple data records within the first data cell; and a second processorcore of the processor may index the multiple data records within thesecond data cell at least partially in parallel with the indexing, bythe first processor core, of the multiple data records within the firstdata cell.

The method may include: for each data cell within the super cell, addinga highest data value and a lowest data value of the range of data valuesof the first data field to a third binary tree; performing an in-ordertraversal of the third binary tree to identify highest and lowest datavalues of the first data field among the data cells within the supercell; adding indications of the highest and lowest values of the firstdata field among the data cells within the super cell to the super cellindex to specify a range of values of the first data field for the supercell therein; for each data cell within the super cell, adding a highestdata value and a lowest data value of the range of data values of thesecond data field to a fourth binary tree; performing an in-ordertraversal of the fourth binary tree to identify highest and lowest datavalues of the second data field among the data cells within the supercell; and adding indications of the highest and lowest values of thesecond data field among the data cells within the super cell to thesuper cell index to specify a range of values of the second data fieldfor the super cell therein.

The method may include, following each retrieval of data values from thefirst data field and the second data field of a data record of themultiple data records, performing operations including: searching afirst binary tree of unique data values of the first data field of thefirst data cell correlated to the identifier of a data record todetermine whether the data value retrieved from the first data fieldincludes a duplicate data value that is already present within the firstbinary tree; adding the data value retrieved from the first data fieldto the first binary tree in response to a determination that the datavalue retrieved from the first data field is a unique data value that isnot already present within the first binary tree; searching a secondbinary tree of unique data values of the second data field of the firstdata cell correlated to the identifier of a data record to determinewhether the data value retrieved from the second data field includes aduplicate data value that is already present within the second binarytree; and adding the data value retrieved from the second data field tothe second binary tree in response to a determination that the datavalue retrieved from the second data field is a unique data value thatis not already present within the second binary tree. The method mayalso include generating the first unique values index based on anin-order traversal of the first binary tree, and generating the secondunique values index based on an in-order traversal of the second binarytree.

The method may include, following each retrieval of a data value fromthe first data field of a data record of the multiple data records,performing operations including: in response to a determination that thefirst data field of the data record stores a duplicate data value,searching a first set of duplicate value indexes within the first cellindex to determine whether a duplicate value index already exists withinthe first set for the duplicate value; in response to identifying anexisting duplicate value index for the duplicate value within the firstset, adding the identifier of the data record to the identified existingduplicate value index; and in response to determining that there is noexisting duplicate value index among the first set of duplicate valueindexes for the duplicate value, adding a duplicate value index to thefirst set for the duplicate value and add the identifier of the datarecord to the added duplicate value index. The method may also include,following each retrieval of a data value from the second data field of adata record of the multiple data records, performing operationsincluding: in response to a determination that the second data field ofthe data record stores a duplicate data value, searching a second set ofduplicate value indexes with the first cell index to determine whether aduplicate value index already exists within the second set for theduplicate value; in response to identifying an existing duplicate valueindex for the duplicate value within the second set, adding theidentifier of the data record to the identified existing duplicate valueindex; and in response to determining that there is no existingduplicate value index among the second set of duplicate value indexesfor the duplicate value, adding a duplicate value index to the secondset for the duplicate value and add the identifier of the data record tothe added duplicate value index. Each duplicate value index within thefirst and second sets of duplicate value indexes includes identifiers ofdata records.

The method may include, transmitting an indication of currentavailability of resources of the first node device to the controldevice, at least partially in parallel with transmissions by other nodedevices of the multiple node devices of availability of resources to thecontrol device, to enable the control device to determine whether toassign the super cell to the first node device in lieu of assigning thesuper cell to another node device of the multiple node devices. Themethod may also include receiving, at the first node device and from thecontrol device, a second pointer to a location within data file fromwhich to retrieve the super cell; and transmitting, to the at least ondata device and at least partially in parallel with other node devicesof the multiple node devices, an instruction to provide the super cellto the first node device.

The method may include: receiving, at the first node device, and atleast partially in parallel with other node devices of the multiple nodedevices, query instructions specifying the search criteria of a searchto be performed of the data set for data records meeting the searchcriteria, wherein the search criteria includes at least one data valueto be searched for within at least one of the first data field and thesecond data field; comparing the at least one data value to at least oneof the range of values of the first data field and the range of valuesof the second data field specified by the super cell index to determinewhether the super cell includes any data records that meet the searchcriteria; and in response to a determination that at least one datarecord within at least one data cell of the super cell does include adata record that meets the search criteria, comparing the at least onedata value to at least one of the range of values of the first datafield and the range of values of the second data field specified by thesuper cell index to determine at least whether the first data cellincludes any data records that meet the search criteria.

The method may include in response to a determination that at least thefirst data cell does include a data record that meets the searchcriteria, using at least one of the first unique values index and thesecond values index to perform a binary search of the data records ofthe first data cell to identify one or more data records of the firstdata cell that meet the search criteria; and upon identifying a datarecord of the first data cell that meets the search criteria, searchingat least one of the first set of duplicate value indexes and the secondset of duplicate value indexes for a duplicate value index thatidentifies one or more additional data records of the first data cellthat meet the search criteria.

The method may include parsing the query instructions to determinewhether the query instructions include task instructions for theperformance of a task with data retrieved from one or more data recordsidentified as meeting the search criteria; and in response to adetermination that the query instructions do include task instructionsfor the performance of a task, executing the instructions to perform thetask at least partially in parallel with at least one other node deviceof the multiple node devices.

An apparatus may include a processor of a first node device of multiplenode devices, and a storage of the first node device to storeinstructions that, when executed by the processor, cause the processorto perform operations including store, at the first node device, a firstsuper cell of multiple super cells into which a data set is divided froma data file maintained by at least one data device, wherein: themultiple super cells are distributed among the multiple node devices;each super cell includes multiple data cells; each data cell of themultiple data cells includes multiple data records; and each data recordof the multiple data records includes a set of fields at which datavalues of the data set are stored. The processor may also be caused tostore, for each data cell within the first super cell, a cell index thatcorresponds to the data cell, wherein the cell index may include anindication of a range of values stored within a first data field of theset of fields among the data records within the data cell; and a firstunique values index that corresponds to the first data field, whereinfor each data value that is stored within the first data field among thedata records within the data cell, the first unique values indexincludes an identifier of a single data record within the data cell inwhich the data value is stored within the first data field. Theprocessor may also be caused to receive, at the first node device, froma control device, and at least partially in parallel with other nodedevices of the multiple node devices, query instructions specifyingsearch criteria of a search to be performed of the data set for datarecords that meet the specified search criteria, wherein the searchcriteria includes at least one data value to be searched for within thefirst data field. The processor may also be caused to, in response tothe receipt of the query instructions, and for each data cell within thefirst super cell, the processor is caused to perform operations of thespecified search, the operations including compare the data value to therange of values indicated in the corresponding cell index to determinewhether the data cell includes at least one data record that meets thespecified search criteria; and in response to a determination that thedata cell includes at least one data record that meets the specifiedsearch criteria, use at least the first unique values index to perform asearch of the data records of the data cell to identify one or more datarecords that meet the search criteria. The processor may also be causedto, in response to identifying at least one data record that meets thespecified search criteria, the processor is caused to perform operationsincluding: generate results data indicative of the first super cellincluding at least one data record that meets the specified searchcriteria; and provide the results data to the control device.

A computer-program product tangibly embodied in a non-transitorymachine-readable storage medium may include instructions operable tocause a processor of a first node device of multiple node devices toperform operations including store, at the first node device, a firstsuper cell of multiple super cells into which a data set is divided froma data file maintained by at least one data device, wherein: themultiple super cells are distributed among the multiple node devices;each super cell includes multiple data cells; each data cell of themultiple data cells includes multiple data records; and each data recordof the multiple data records includes a set of fields at which datavalues of the data set are stored. The processor may also be caused tostore, for each data cell within the first super cell, a cell index thatcorresponds to the data cell, wherein the cell index may include anindication of a range of values stored within a first data field of theset of fields among the data records within the data cell; and a firstunique values index that corresponds to the first data field, whereinfor each data value that is stored within the first data field among thedata records within the data cell, the first unique values indexincludes an identifier of a single data record within the data cell inwhich the data value is stored within the first data field. Theprocessor may also be caused to receive, at the first node device, froma control device, and at least partially in parallel with other nodedevices of the multiple node devices, query instructions specifyingsearch criteria of a search to be performed of the data set for datarecords that meet the specified search criteria, wherein the searchcriteria includes at least one data value to be searched for within thefirst data field. The processor may also be caused to, in response tothe receipt of the query instructions, and for each data cell within thefirst super cell, the processor is caused to perform operations of thespecified search, the operations including compare the data value to therange of values indicated in the corresponding cell index to determinewhether the data cell includes at least one data record that meets thespecified search criteria; and in response to a determination that thedata cell includes at least one data record that meets the specifiedsearch criteria, use at least the first unique values index to perform asearch of the data records of the data cell to identify one or more datarecords that meet the search criteria. The processor may also be causedto, in response to identifying at least one data record that meets thespecified search criteria, the processor is caused to perform operationsincluding: generate results data indicative of the first super cellincluding at least one data record that meets the specified searchcriteria; and provide the results data to the control device.

The multiple data cells of the first super cell may include a first datacell and a second data cell; the processor may be caused to perform thespecified search within the first data cell on a first thread ofexecution; and the processor may be caused to perform the specifiedsearch within the second data cell on a second thread of execution. Theprocessor may also be caused to allocate a separate processor core ofthe processor to each of the first and second threads of execution.

A cell index corresponding to a data cell of the first super cell mayinclude a first set of duplicate value indexes, wherein for at least onedata value that is stored within the first data field of a data recordidentified in the first unique values index, a duplicate value index ofthe first set of duplicate value indexes includes at least oneidentifier of an additional data record within the data cell in whichthe data value is also stored within the first data field. The processormay be caused, in response to identifying at least one data recordwithin the data cell that meets the specified search criteria, toperform operations including: search within the first set of duplicatevalue indexes for a duplicate value index that identifies one or moreadditional data records of the data cell that also meet the specifiedsearch criteria; and generate the results data to be indicative of theone or more additional data records.

The cell index may include a second unique values index that correspondsto a second data field of the set of fields within the data records ofthe data cell; for each data value that is stored within the second datafield among the data records within the data cell, the second uniquevalues index includes an identifier of a single data record within thedata cell in which the data value is stored within the second datafield; the cell index may include a second set of duplicate valueindexes, wherein for at least one data value that is stored within thesecond data field of a data record identified in the second uniquevalues index, a duplicate value index of the second set of duplicatevalue indexes includes at least one identifier of an additional datarecord within the data cell in which the data value is also storedwithin the first data field; the first unique values index may include acount of identifiers of data records included in the first unique valuesindex; the second unique values index may include a count of identifiersof data records included in the second unique values index; eachduplicate value index within the first set of duplicate value indexesmay include a count of identifiers of data records included in theduplicate value index; each duplicate value index within the second setof duplicate value indexes may include a count of identifiers of datarecords included in the duplicate value index; and the search criteriamay include at least one data value that to be searched for within thesecond data field. The processor may be caused to perform operationsincluding: analyze the count of identifiers of data records within thefirst unique values index, the second unique values index, eachduplicate value index within the first set of duplicate value indexesand each duplicate value index within the second set of duplicate valueindexes to determine relative degrees of cardinality of the data valuesof the first data field and the second data field; and determine whetherto begin the performance of the specified search of the data recordswithin the data cell with the first unique values index or the secondunique values index based on the relative degrees of cardinality of thedata values of the first data field and the second data field.

The processor is caused to perform operations including parse the queryinstructions to determine whether the query instructions include taskinstructions for the performance of a task with data retrieved from oneor more data records identified as meeting the search criteria; and inresponse to a determination that the query instructions do include taskinstructions for the performance of a task, the processor may be causedto perform operations including execute the instructions to perform thetask at least partially in parallel with at least one other node deviceof the multiple node devices, and generate the results data to includeresults of the performance of the task as the indication that the supercell includes at least one data record that meets the specified searchcriteria.

The processor may be caused to perform operations including: store, atthe first node device, a first super cell index corresponding to thefirst super cell, wherein the first super cell index may include anindication of a range of values stored within the first data fieldwithin the multiple data cells of the first super cell; in response tothe receipt of the query instructions, compare the at least one datavalue of the search criteria to the range of values indicated in thefirst super cell index to determine whether the first super cellincludes at least one data record within at least one data cell of thefirst super cell that meets the specified search criteria; and conditionthe performance of the operations of the specified search for each datacell within the first super cell on a determination that the first supercell does include at least one data record within at least one data cellof the first super cell that meets the specified search criteria.

The first node device may include a controller, and the controller mayinclude a controller processor and a controller storage to store otherinstructions that, when executed by the controller processor, cause thecontroller processor to perform operations to serve as the controldevice. The operations may include: receive, at the first node deviceand from a second node device of the multiple node devices, a secondsuper cell index corresponding to a second super cell stored by thesecond node device, wherein the second super cell index may include anindication of a range of values stored within the first data fieldwithin the at least one data cell of the second super cell; in responseto the receipt of the query instructions, compare the data value to therange of values indicated in the second super cell index to determinewhether the second super cell includes at least one data record withinat least one data cell of the second super cell that meets the specifiedsearch criteria; and in response to a determination that the secondsuper cell includes at least one data record that meets the specifiedsearch criteria, transmit the query instructions to the second nodedevice to enable the second node device to perform the specified searchwithin the at least one data cell of the second super cell.

The processor may be caused to perform operations including: store,within each cell index corresponding to a data cell of the multiple datacells within the first super cell, a unique values vector that mayinclude a single instance of each data value that is stored within thefirst data field among the data records within the corresponding datacell, wherein the single instances of data values within the uniquevalues vector within each cell index are sorted by value; in response tothe receipt of the query instructions, compare the at least one datavalue of the search criteria to at least one of the single instances ofdata values within the unique values vector within each cell index todetermine whether the first super cell includes at least one data cellthat meets the specified search criteria; and condition the performanceof the operations of the specified search for each data cell within thefirst super cell on a determination that the first super cell doesinclude at least one data cell that meets the specified search criteria.

The processor is caused to receive, at the first node device, the firstsuper cell from a data file maintained by at least one data device. Theprocessor may also be caused to index, at the first node device, and atleast partially in parallel with other node devices of the multiple nodedevices, the multiple data records within each data cell of the multipledata cells by the first data field of the set of fields in a single readpass through each data cell of the multiple data cells, wherein for eachdata record within the data cell, the processor may be caused to:retrieve a data value from the first data field; determine, based on thedata value retrieved from the first data field, whether the first datafield of the data record stores a unique data value that has not yetretrieved by the processor from the first data field of any data recordof the data cell; and in response to a determination that the first datafield of the data record stores a unique data value, add an identifierof the data record to the first unique values index, wherein identifiersof data records within the first unique values index are ordered into avector of identifiers based on an ordering of the corresponding uniquedata values in the first data field that is selected to enable use ofthe first unique values index to perform the search of the data recordsof the data cell. The processor may be further caused to generate,within a super cell index corresponding to the super cell, an indicationof a range of the data values of the first data field within the datarecords of the data cell to enable use of the super cell index todetermine whether the at least one data value of the search criteria isare present within the first data field of any data record of the datacell.

A computer-implemented method may include storing, at a first nodedevice of multiple node devices, a first super cell of multiple supercells into which a data set is divided from a data file maintained by atleast one data device, wherein: the multiple super cells are distributedamong the multiple node devices; each super cell may include multipledata cells; each data cell of the multiple data cells may includemultiple data records; and each data record of the multiple data recordsmay include a set of fields at which data values of the data set arestored. The method may also include: storing, for each data cell withinthe first super cell, a cell index that corresponds to the data cell,wherein the cell index includes: an indication of a range of valuesstored within a first data field of the set of fields among the datarecords within the data cell; and a first unique values index thatcorresponds to the first data field, wherein for each data value that isstored within the first data field among the data records within thedata cell, the first unique values index includes an identifier of asingle data record within the data cell in which the data value isstored within the first data field. The method may also includereceiving, at the first node device, from a control device, and at leastpartially in parallel with other node devices of the multiple nodedevices, query instructions specifying search criteria of a search to beperformed of the data set for data records that meet the specifiedsearch criteria, wherein the search criteria may include at least onedata value to be searched for within the first data field. The methodmay further include, in response to the receipt of the queryinstructions, and for each data cell within the first super cell,performing operations of the specified search, the operations including:comparing the at least one data value of the search criteria to therange of values indicated in the corresponding cell index to determinewhether the data cell includes at least one data record that meets thespecified search criteria; and in response to a determination that thedata cell includes at least one data record that meets the specifiedsearch criteria, using at least the first unique values index to performa search of the data records of the data cell to identify one or moredata records that meet the search criteria. The method may furtherinclude, in response to identifying at least one data record that meetsthe specified search criteria, performing operations includinggenerating results data indicative of the first super cell including atleast one data record that meets the specified search criteria, andproviding the results data to the control device.

The multiple data cells of the first super cell may include a first datacell and a second data cell, and the method may include: performing thespecified search within the first data cell on a first thread ofexecution of a processor of the first node device and performing thespecified search within the second data cell on a second thread ofexecution of the processor. The method may also include allocating aseparate processor core of the processor to each of the first and secondthreads of execution.

A cell index corresponding to a data cell of the first super cell mayinclude a first set of duplicate value indexes, wherein for at least onedata value that is stored within the first data field of a data recordidentified in the first unique values index, a duplicate value indexwithin the first set of duplicate value indexes includes at least oneidentifier of an additional data record within the data cell in whichthe data value is also stored within the first data field. The methodmay include, in response to identifying at least one data record withinthe data cell that meets the specified search criteria, performingoperations including: searching within the first set of duplicate valueindexes for a duplicate value index that identifies one or moreadditional data records of the data cell that also meet the specifiedsearch criteria; and generating the results data to be indicative of theone or more additional data records.

The cell index may include a second unique values index that correspondsto a second data field of the set of fields within the data records ofthe data cell; for each data value that is stored within the second datafield among the data records within the data cell, the second uniquevalues index includes an identifier of a single data record within thedata cell in which the data value is stored within the second datafield; the cell index may include a second set of duplicate valueindexes, wherein for at least one data value that is stored within thesecond data field of a data record identified in the second uniquevalues index, a duplicate value index of the second set of duplicatevalue indexes includes at least one identifier of an additional datarecord within the data cell in which the data value is also storedwithin the first data field; the first unique values index may include acount of identifiers of data records included in the first unique valuesindex; the second unique values index may include a count of identifiersof data records included in the second unique values index; eachduplicate value index within the first set of duplicate value indexesmay include a count of identifiers of data records included in theduplicate value index; each duplicate value index within the second setof duplicate value indexes may include a count of identifiers of datarecords included in the duplicate value index; and the search criteriamay include at least one data value that to be searched for within thesecond data field. The method may include: analyzing the count ofidentifiers of data records within the first unique values index, thesecond unique values index, each duplicate value index within the firstset of duplicate value indexes and each duplicate value index within thesecond set of duplicate value indexes to determine relative degrees ofcardinality of the data values of the first data field and the seconddata field; and determining whether to begin the performance of thespecified search of the data records within the data cell with the firstunique values index or the second unique values index based on therelative degrees of cardinality of the data values of the first datafield and the second data field.

The method may include parsing the query instructions to determinewhether the query instructions include task instructions for theperformance of a task with data retrieved from one or more data recordsidentified as meeting the search criteria; and in response to adetermination that the query instructions do include task instructionsfor the performance of a task, performing operations includingexecuting, at the first node device, the instructions to perform thetask at least partially in parallel with at least one other node deviceof the multiple node devices, and generating the results data to includeresults of the performance of the task as the indication that the supercell includes at least one data record that meets the specified searchcriteria.

The method may include: storing, at the first node device, a first supercell index corresponding to the first super cell, wherein the firstsuper cell index may include an indication of a range of values storedwithin the first data field within the multiple data cells of the firstsuper cell; in response to the receipt of the query instructions,comparing the at least one data value of the search criteria to therange of values indicated in the first super cell index to determinewhether the first super cell includes at least one data record within atleast one data cell of the first super cell that meets the specifiedsearch criteria; and conditioning the performance of the operations ofthe specified search for each data cell within the first super cell on adetermination that the first super cell does include at least one datarecord within at least one data cell of the first super cell that meetsthe specified search criteria.

The first node device may include a controller, and the controller mayinclude a controller processor and a controller storage to store otherinstructions that, when executed by the controller processor, cause thecontroller processor to perform operations to serve as the controldevice. The operations may include: receiving, at the first node deviceand from a second node device of the multiple node devices, a secondsuper cell index corresponding to a second super cell stored by thesecond node device, wherein the second super cell index may include anindication of a range of values stored within the first data fieldwithin the at least one data cell of the second super cell; in responseto the receipt of the query instructions, comparing the at least onedata value of the search criteria to the range of values indicated inthe second super cell index to determine whether the second super cellincludes at least one data record within at least one data cell of thesecond super cell that meets the specified search criteria; and inresponse to a determination that the second super cell includes at leastone data record that meets the specified search criteria, transmittingthe query instructions to the second node device to enable the secondnode device to perform the specified search within the at least one datacell of the second super cell.

The method may include: storing, within each cell index corresponding toa data cell of the multiple data cells within the first super cell, aunique values vector that may include a single instance of each datavalue that is stored within the first data field among the data recordswithin the corresponding data cell, wherein the single instances of datavalues within the unique values vector within each cell index are sortedby value; in response to the receipt of the query instructions,comparing the at least one data value of the search criteria to at leastone of the single instances of data values within the unique valuesvector within each cell index to determine whether the first super cellincludes at least one data cell that meets the specified searchcriteria; and conditioning the performance of the operations of thespecified search for each data cell within the first super cell on adetermination that the first super cell does include at least one datacell that meets the specified search criteria.

The method may include receiving, at the first node device, the firstsuper cell from a data file maintained by at least one data device. Themethod may also include indexing, at the first node device, and at leastpartially in parallel with other node devices of the multiple nodedevices, the multiple data records within each data cell of the multipledata cells by the first data field of the set of fields in a single readpass through each data cell of the multiple data cells, wherein for eachdata record within the data cell, the method may include: retrieving adata value from the first data field; determining, based on the datavalue retrieved from the first data field, whether the first data fieldof the data record stores a unique data value that has not yet retrievedby a processor of the first node device from the first data field of anydata record of the data cell; and in response to a determination thatthe first data field of the data record stores a unique data value,adding an identifier of the data record to the first unique valuesindex, wherein identifiers of data records within the first uniquevalues index are ordered into a vector of identifiers based on anordering of the corresponding unique data values in the first data fieldthat is selected to enable use of the first unique values index toperform the search of the data records of the data cell. The method mayfurther include generating, within a super cell index corresponding tothe super cell, an indication of a range of the data values of the firstdata field within the data records of the data cell to enable use of thesuper cell index to determine whether the at least one data value of thesearch criteria is are present within the first data field of any datarecord of the data cell.

An apparatus may include a processor of a first node device of multiplenode devices, and a storage of the first node device to storeinstructions that, when executed by the processor, cause the processorto perform operations including store, at the first node device, a supercell of multiple super cells into which a data set is divided from adata file maintained by at least one data device, wherein: the multiplesuper cells are distributed among the multiple node devices; each supercell may include multiple data cells; each data cell of the multipledata cells may include multiple data records; and each data record ofthe multiple data records may include a set of data fields at which datavalues of the data set are stored. The processor may also be caused toperform operations including: index, at the first node device, themultiple data records within a first data cell of a super cell by afirst data field and by a second data field of the set of data fields ina single read pass through the first data cell, wherein for each datarecord within the first data cell, the processor is caused to: retrievedata values from the first data field and the second data field; performoperations to generate a first binary tree of unique data values of thefirst data field of the first data cell to determine whether the datavalue retrieved from the first data field may include a unique value ora duplicate value, the operations including search the first binary treeto determine whether the data value retrieved from the first data fieldmay include a duplicate data value that is already present within thefirst binary tree or a unique value that is not already present withinthe first binary tree, and add the data value retrieved from the firstdata field to the first binary tree in response to a determination thatthe data value retrieved from the first data field is a unique value;and perform operations to generate a second binary tree of unique datavalues of the second data field of the first data cell to determinewhether the data value retrieved from the second data field may includea unique value or a duplicate value, the operations including search thesecond binary tree to determine whether the data value retrieved fromthe second data field may include a duplicate data value that is alreadypresent within the second binary tree or a unique value that is notalready present within the second binary tree, and add the data valueretrieved from the second data field to the second binary tree inresponse to a determination that the data value retrieved from thesecond data field is a unique value. The processor may be further causedto perform operations including: generate a first unique values index ofthe indexes of the data records associated with the unique data valueswithin the first binary tree based on an in-order traversal of the firstbinary tree to enable use of the first unique values index to perform abinary search of the data values within the first data field of the datarecords of the first data cell; generate a second unique values index ofthe indexes of the data records associated with the unique data valueswithin the second binary tree based on an in-order traversal of thesecond binary tree to enable use of the second unique values index toperform a binary search of the data values within the second data fieldof the data records of the first data cell; generate, within a supercell index corresponding to the super cell, and from the in-ordertraversals of the first and second binary trees, an indication of arange of the data values of the first data field within the data recordsof the first data cell, and an indication of a range of the data valuesof the second data field within the data records of the first data cell;and provide the super cell index to a control device to enable use ofthe super cell index by the control device.

A computer-program product tangibly embodied in a non-transitorymachine-readable storage medium, the computer-program product includinginstructions operable to cause a processor of a first node device ofmultiple node devices to perform operations including store, at thefirst node device, a super cell of multiple super cells into which adata set is divided from a data file maintained by at least one datadevice, wherein: the multiple super cells are distributed among themultiple node devices; each super cell may include multiple data cells;each data cell of the multiple data cells may include multiple datarecords; and each data record of the multiple data records may include aset of data fields at which data values of the data set are stored. Theprocessor may also be caused to perform operations including: index, atthe first node device, the multiple data records within a first datacell of a super cell by a first data field and by a second data field ofthe set of data fields in a single read pass through the first datacell, wherein for each data record within the first data cell, theprocessor is caused to: retrieve data values from the first data fieldand the second data field; perform operations to generate a first binarytree of unique data values of the first data field of the first datacell to determine whether the data value retrieved from the first datafield may include a unique value or a duplicate value, the operationsincluding search the first binary tree to determine whether the datavalue retrieved from the first data field may include a duplicate datavalue that is already present within the first binary tree or a uniquevalue that is not already present within the first binary tree, and addthe data value retrieved from the first data field to the first binarytree in response to a determination that the data value retrieved fromthe first data field is a unique value; and perform operations togenerate a second binary tree of unique data values of the second datafield of the first data cell to determine whether the data valueretrieved from the second data field may include a unique value or aduplicate value, the operations including search the second binary treeto determine whether the data value retrieved from the second data fieldmay include a duplicate data value that is already present within thesecond binary tree or a unique value that is not already present withinthe second binary tree, and add the data value retrieved from the seconddata field to the second binary tree in response to a determination thatthe data value retrieved from the second data field is a unique value.The processor may be further caused to perform operations including:generate a first unique values index of the indexes of the data recordsassociated with the unique data values within the first binary treebased on an in-order traversal of the first binary tree to enable use ofthe first unique values index to perform a binary search of the datavalues within the first data field of the data records of the first datacell; generate a second unique values index of the indexes of the datarecords associated with the unique data values within the second binarytree based on an in-order traversal of the second binary tree to enableuse of the second unique values index to perform a binary search of thedata values within the second data field of the data records of thefirst data cell; generate, within a super cell index corresponding tothe super cell, and from the in-order traversals of the first and secondbinary trees, an indication of a range of the data values of the firstdata field within the data records of the first data cell, and anindication of a range of the data values of the second data field withinthe data records of the first data cell; and provide the super cellindex to a control device to enable use of the super cell index by thecontrol device.

The super cell may include a second data cell in addition to the firstdata cell; the processor may be caused to index, on a first thread ofexecution, the multiple data records within the first data cell togenerate a first cell index that corresponds to the first data cell; andthe processor may be caused to index, on a second thread of execution atleast partially in parallel with the indexing of the multiple datarecords of the first data cell, the multiple data records within thesecond data cell by the first data field and the by the second datafield of the set of data fields in a single read pass to generate asecond cell index that corresponds to the second data cell, wherein thesecond cell index includes unique values indexes to enable a binarysearch of the data values within at least one of the first data fieldand the second data field of the data records within the second datacell. The processor may be caused to allocate a separate processor coreof the processor to each of the first and second threads of execution.

The processor may be caused to perform operations including: for eachdata cell within the super cell, add a highest data value and a lowestdata value of the range of data values of the first data field to athird binary tree; perform an in-order traversal of the third binarytree to identify highest and lowest data values of the first data fieldamong the data cells within the super cell; add indications of thehighest and lowest values of the first data field among the data cellswithin the super cell to the super cell index to specify a range ofvalues of the first data field for the super cell therein; for each datacell within the super cell, add a highest data value and a lowest datavalue of the range of data values of the second data field to a fourthbinary tree; perform an in-order traversal of the fourth binary tree toidentify highest and lowest data values of the second data field amongthe data cells within the super cell; and add indications of the highestand lowest values of the second data field among the data cells withinthe super cell to the super cell index to specify a range of values ofthe second data field for the super cell therein.

The processor may be caused to perform operations including: analyze adata type and a data size of the first data field to determine whetherto generate a unique values vector within a first cell index thatcorresponds to the first data cell; and in response to a determinationto generate the unique values vector, the processor is caused toretrieve the unique values within the first binary tree via an in-ordertraversal of the first binary tree, and generate the unique valuesvector to include the unique values within the first binary tree sortedas sorted by the in-order traversal.

The processor may be caused to perform operations including: analyze adata type and a data size of the first data field to determine whetherto generate a hash values vector within a first cell index thatcorresponds to the first data cell; and in response to a determinationto generate the hash values vector, the processor is caused to generatea hash value from each unique value within the first binary tree, andgenerate the hash values vector to include the hash values sorted byvalue.

Following each retrieval of a data value from the first data field of adata record of the multiple data records, the processor is caused toperform operations including: in response to a determination that thefirst data field of the data record stores a duplicate data value,search a first set of duplicate value indexes within the first cellindex to determine whether a duplicate value index already exists withinthe first set for the duplicate value; in response to identifying anexisting duplicate value index for the duplicate value within the firstset, add the identifier of the data record to the identified existingduplicate value index; and in response to determining that there is noexisting duplicate value index among the first set of duplicate valueindexes for the duplicate value, add a duplicate value index to thefirst set for the duplicate value and add the identifier of the datarecord to the added duplicate value index. Following each retrieval of adata value from the second data field of a data record of the multipledata records, the processor is caused to perform operations including:in response to a determination that the second data field of the datarecord stores a duplicate data value, search a second set of duplicatevalue indexes with the first cell index to determine whether a duplicatevalue index already exists within the second set for the duplicatevalue; in response to identifying an existing duplicate value index forthe duplicate value within the second set, add the identifier of thedata record to the identified existing duplicate value index; and inresponse to determining that there is no existing duplicate value indexamong the second set of duplicate value indexes for the duplicate value,add a duplicate value index to the second set for the duplicate valueand add the identifier of the data record to the added duplicate valueindex. The first unique values index may a vector of identifiers of datarecords; the second unique values index may include a vector ofidentifiers of data records; and each duplicate value index within thefirst and second sets of duplicate value indexes may include a vector ofidentifiers of data records.

The first unique values index may include a count of indexes of datarecords included in the first unique values index; the second uniquevalues index may include a count of indexes of data records included inthe second unique values index; each duplicate value index within thefirst set of duplicate value indexes may include a count of indexes ofdata records included in the duplicate value index; and each duplicatevalue index within the second set of duplicate value indexes may includea count of indexes of data records included in the duplicate valueindex.

The processor may be caused to perform operations including: receive, atthe first node device, and at least partially in parallel with othernode devices of the multiple node devices, query instructions specifyingsearch criteria of a search to be performed of the data set for datarecords meeting the search criteria, wherein the search criteria mayinclude at least one data value to be searched for within at least oneof the first data field and the second data field; compare the at leastone data value to at least one of the range of values of the first datafield and the range of values of the second data field specified by thesuper cell index to determine whether the super cell includes any datarecords that meet the search criteria; and in response to adetermination that at least one data record within at least one datacell of the super cell does include a data record that meets the searchcriteria, compare the at least one data value to at least one of therange of values of the first data field and the range of values of thesecond data field specified by the super cell index to determine atleast whether the first data cell includes any data records that meetthe search criteria.

The processor may be caused to perform operations including: in responseto a determination that at least the first data cell does include a datarecord that meets the search criteria, use at least one of the firstunique values index and the second values index to perform a binarysearch of the data records of the first data cell to identify one ormore data records of the first data cell that meet the search criteria;and upon identifying a data record of the first data cell that meets thesearch criteria, search at least one of the first set of duplicate valueindexes and the second set of duplicate value indexes for a duplicatevalue index that identifies one or more additional data records of thefirst data cell that meet the search criteria.

A computer-implemented method includes storing, at the first nodedevice, a super cell of multiple super cells into which a data set isdivided from a data file maintained by at least one data device,wherein: the multiple super cells are distributed among the multiplenode devices; each super cell includes multiple data cells; each datacell of the multiple data cells includes multiple data records; and eachdata record of the multiple data records includes a set of data fieldsat which data values of the data set are stored. The method may alsoinclude indexing, at the first node device, the multiple data recordswithin a first data cell of a super cell by a first data field and by asecond data field of the set of data fields in a single read passthrough the first data cell, wherein the operations may include, foreach data record within the first data cell: retrieving data values fromthe first data field and the second data field; performing operations togenerate a first binary tree of unique data values of the first datafield of the first data cell to determine whether the data valueretrieved from the first data field may include a unique value or aduplicate value, the operations including searching the first binarytree to determine whether the data value retrieved from the first datafield may include a duplicate data value that is already present withinthe first binary tree or a unique value that is not already presentwithin the first binary tree; and adding the data value retrieved fromthe first data field to the first binary tree in response to adetermination that the data value retrieved from the first data field isa unique value; and perform operations to generate a second binary treeof unique data values of the second data field of the first data cell todetermine whether the data value retrieved from the second data fieldmay include a unique value or a duplicate value, the operationsincluding searching the second binary tree to determine whether the datavalue retrieved from the second data field may include a duplicate datavalue that is already present within the second binary tree or a uniquevalue that is not already present within the second binary tree andadding the data value retrieved from the second data field to the secondbinary tree in response to a determination that the data value retrievedfrom the second data field is a unique value. The method may furtherinclude: generating a first unique values index of the indexes of thedata records associated with the unique data values within the firstbinary tree based on an in-order traversal of the first binary tree toenable use of the first unique values index to perform a binary searchof the data values within the first data field of the data records ofthe first data cell; generating a second unique values index of theindexes of the data records associated with the unique data valueswithin the second binary tree based on an in-order traversal of thesecond binary tree to enable use of the second unique values index toperform a binary search of the data values within the second data fieldof the data records of the first data cell; generating, within a supercell index corresponding to the super cell, and from the in-ordertraversals of the first and second binary trees, an indication of arange of the data values of the first data field within the data recordsof the first data cell, and an indication of a range of the data valuesof the second data field within the data records of the first data cell;and providing the super cell index to a control device to enable use ofthe super cell index by the control device.

The super cell may include a second data cell in addition to the firstdata cell; and the method may include: indexing, on a first thread ofexecution, the multiple data records within the first data cell togenerate a first cell index that corresponds to the first data cell; andindexing, on a second thread of execution at least partially in parallelwith the indexing the multiple data records of the first data cell, themultiple data records within the second data cell by the first datafield and the by the second data field of the set of data fields in asingle read pass to generate a second cell index that corresponds to thesecond data cell, wherein the second cell index includes unique valuesindexes to enable a binary search of the data values within at least oneof the first data field and the second data field of the data recordswithin the second data cell. The method may also include allocating aseparate processor core of the processor to each of the first and secondthreads of execution.

The method may include: for each data cell within the super cell, addinga highest data value and a lowest data value of the range of data valuesof the first data field to a third binary tree; performing an in-ordertraversal of the third binary tree to identify highest and lowest datavalues of the first data field among the data cells within the supercell; adding indications of the highest and lowest values of the firstdata field among the data cells within the super cell to the super cellindex to specify a range of values of the first data field for the supercell therein; for each data cell within the super cell, adding a highestdata value and a lowest data value of the range of data values of thesecond data field to a fourth binary tree; performing an in-ordertraversal of the fourth binary tree to identify highest and lowest datavalues of the second data field among the data cells within the supercell; and adding indications of the highest and lowest values of thesecond data field among the data cells within the super cell to thesuper cell index to specify a range of values of the second data fieldfor the super cell therein.

The method may include: analyzing a data type and a data size of thefirst data field to determine whether to generate a unique values vectorwithin a first cell index that corresponds to the first data cell; andin response to a determination to generate the unique values vector, theprocessor is caused to perform operations including retrieving theunique values within the first binary tree via an in-order traversal ofthe first binary tree, and generating the unique values vector toinclude the unique values within the first binary tree sorted as sortedby the in-order traversal.

The method may include: analyzing a data type and a data size of thefirst data field to determine whether to generate a hash values vectorwithin a first cell index that corresponds to the first data cell; andin response to a determination to generate the hash values vector, theprocessor is caused to perform operations including generating a hashvalue from each unique value within the first binary tree, andgenerating the hash values vector to include the hash values sorted byvalue.

The method may include, following each retrieval of a data value fromthe first data field of a data record of the multiple data records,performing operations including: in response to a determination that thefirst data field of the data record stores a duplicate data value,searching a first set of duplicate value indexes within the first cellindex to determine whether a duplicate value index already exists withinthe first set for the duplicate value; in response to identifying anexisting duplicate value index for the duplicate value within the firstset, adding the identifier of the data record to the identified existingduplicate value index; and in response to determining that there is noexisting duplicate value index among the first set of duplicate valueindexes for the duplicate value, adding a duplicate value index to thefirst set for the duplicate value and add the identifier of the datarecord to the added duplicate value index. The method may also include,following each retrieval of a data value from the second data field of adata record of the multiple data records, performing operationsincluding: in response to a determination that the second data field ofthe data record stores a duplicate data value, searching a second set ofduplicate value indexes with the first cell index to determine whether aduplicate value index already exists within the second set for theduplicate value; in response to identifying an existing duplicate valueindex for the duplicate value within the second set, adding theidentifier of the data record to the identified existing duplicate valueindex; and in response to determining that there is no existingduplicate value index among the second set of duplicate value indexesfor the duplicate value, adding a duplicate value index to the secondset for the duplicate value and add the identifier of the data record tothe added duplicate value index. The first unique values index mayinclude a vector of identifiers of data records; the second uniquevalues index may include a vector of identifiers of data records; andeach duplicate value index within the first and second sets of duplicatevalue indexes may include a vector of identifiers of data records.

The first unique values index may include a count of indexes of datarecords included in the first unique values index; the second uniquevalues index may include a count of indexes of data records included inthe second unique values index; each duplicate value index within thefirst set of duplicate value indexes may include a count of indexes ofdata records included in the duplicate value index; and each duplicatevalue index within the second set of duplicate value indexes may includea count of indexes of data records included in the duplicate valueindex.

The method may include: receiving, at the first node device, and atleast partially in parallel with other node devices of the multiple nodedevices, query instructions specifying search criteria of a search to beperformed of the data set for data records meeting the search criteria,wherein the search criteria may include at least one data value to besearched for within at least one of the first data field and the seconddata field; comparing the at least one data value to at least one of therange of values of the first data field and the range of values of thesecond data field specified by the super cell index to determine whetherthe super cell includes any data records that meet the search criteria;and in response to a determination that at least one data record withinat least one data cell of the super cell does include a data record thatmeets the search criteria, comparing the at least one data value to atleast one of the range of values of the first data field and the rangeof values of the second data field specified by the super cell index todetermine at least whether the first data cell includes any data recordsthat meet the search criteria.

The method may include: in response to a determination that at least thefirst data cell does include a data record that meets the searchcriteria, using at least one of the first unique values index and thesecond values index to perform a binary search of the data records ofthe first data cell to identify one or more data records of the firstdata cell that meet the search criteria; and upon identifying a datarecord of the first data cell that meets the search criteria, searchingat least one of the first set of duplicate value indexes and the secondset of duplicate value indexes for a duplicate value index thatidentifies one or more additional data records of the first data cellthat meet the search criteria.

An apparatus includes a processor of a first node device of multiplenode devices, and a storage of the first node device to storeinstructions that, when executed by the processor, cause the processorto perform operations including store, at the first node device, a firstsuper cell of multiple super cells into which a data set is divided froma data file maintained by at least one data device, wherein: themultiple super cells are distributed among the multiple node devices;each super cell includes multiple data cells; each data cell of themultiple data cells includes multiple data records; and each data recordof the multiple data records includes a set of data fields at which datavalues of the data set are stored. The processor may also be caused tostore, for each data cell within the first super cell, a cell index thatcorresponds to the data cell, wherein the cell index may include a firsthash values vector that corresponds to a first data field of the set ofdata fields, and that may include hash values generated from each uniquevalue among the data values stored within the first data field; andreceive, at the first node device, from a control device, and at leastpartially in parallel with other node devices of the multiple nodedevices, query instructions specifying search criteria of a search to beperformed of the data set for data records that meet the searchcriteria, wherein the search criteria may include at least one datavalue to be searched for within the first data field. The processor mayalso be caused to, in response to the receipt of the query instructions,and for each data cell within the first super cell, the processor iscaused to perform operations of the search, the operations including:generate a first hash value from a first data value of the at least onedata value of the search criteria; compare the first hash value to thehash values within the first hash values vector in the correspondingcell index to determine whether the data cell includes at least one datarecord that meets the search criteria for at least the first data value;and in response to a determination that the data cell includes at leastone data record that meets the search criteria, search the data recordsof the data cell to identify one or more data records that meet thesearch criteria. The processor may further be caused to, in response toidentifying at least one data record within at least one data cell ofthe first super cell that meets the search criteria for at least thefirst data value, the processor is caused to perform operationsincluding: generate results data indicative of the first super cellincluding at least one data record that meets the search criteria for atleast the first data value; and provide the results data to the controldevice.

A computer-program product tangibly embodied in a non-transitorymachine-readable storage medium may include instructions operable tocause a processor of a first node device of multiple node devices toperform operations including store, at the first node device, a firstsuper cell of multiple super cells into which a data set is divided froma data file maintained by at least one data device, wherein: themultiple super cells are distributed among the multiple node devices;each super cell includes multiple data cells; each data cell of themultiple data cells includes multiple data records; and each data recordof the multiple data records includes a set of data fields at which datavalues of the data set are stored. The processor may also be caused tostore, for each data cell within the first super cell, a cell index thatcorresponds to the data cell, wherein the cell index may include a firsthash values vector that corresponds to a first data field of the set ofdata fields, and that may include hash values generated from each uniquevalue among the data values stored within the first data field; andreceive, at the first node device, from a control device, and at leastpartially in parallel with other node devices of the multiple nodedevices, query instructions specifying search criteria of a search to beperformed of the data set for data records that meet the searchcriteria, wherein the search criteria may include at least one datavalue to be searched for within the first data field. The processor mayalso be caused to, in response to the receipt of the query instructions,and for each data cell within the first super cell, the processor iscaused to perform operations of the search, the operations including:generate a first hash value from a first data value of the at least onedata value of the search criteria; compare the first hash value to thehash values within the first hash values vector in the correspondingcell index to determine whether the data cell includes at least one datarecord that meets the search criteria for at least the first data value;and in response to a determination that the data cell includes at leastone data record that meets the search criteria, search the data recordsof the data cell to identify one or more data records that meet thesearch criteria. The processor may further be caused to, in response toidentifying at least one data record within at least one data cell ofthe first super cell that meets the search criteria for at least thefirst data value, the processor is caused to perform operationsincluding: generate results data indicative of the first super cellincluding at least one data record that meets the search criteria for atleast the first data value; and provide the results data to the controldevice.

Each of the cell indexes corresponding to the data cells within thefirst super cell may include a second hash values vector thatcorresponds to a second data field of the set of data fields, whereinthe second hash values vector may include hash values generated fromeach unique value among the data values stored within the second datafield. In response to identifying at least one data record within atleast one data cell of the first super cell that meets the searchcriteria for at least the first data value, and for each data cellwithin the at least one data cell, the processor may be caused toperform operations of the search, the operations including: generate asecond hash value from a second data value of the at least one datavalue of the search criteria; compare the second hash value to the hashvalues within the second hash values vector in the corresponding cellindex to determine whether the data cell includes at least one datarecord that meets the search criteria for at least the first data valueand the second data value; and in response to a determination that thedata cell includes at least one data record that meets the searchcriteria for at least the first data value and the second data value,search the data records of the data cell to identify one or more datarecords that meet the search criteria for at least the first data valueand the second data value. The processor may also be caused to conditionthe generation and transmission of results on identification of at leastone data record within at least one data cell of the first super cellthat meets the search criteria for the first data value and the seconddata value.

The processor may be caused to perform operations including perform thesearch corresponding to the first data field on a first thread ofexecution, and perform the search corresponding to the second data fieldon a second thread of execution at least partially in parallel with theperformance of the search on the first thread. The processor may alsocaused to allocate a separate processor core of the processor to each ofthe first and second threads of execution.

Each of the cell indexes corresponding to the data cells within thefirst super cell may include a unique values vector that corresponds tothe first data field, wherein the unique values vector may include asingle instance of each data values present within the first data fieldamong the data records of the corresponding data cell, wherein thesingle instances of each data value are sorted by value. In response toidentifying at least one data record within at least one data cell ofthe first super cell that meets the search criteria for at least thefirst data value, and for each data cell within the at least one datacell, the processor may be caused to perform operations of the search,the operations including: compare the first data value of the at leastone data value of the search criteria to the single instances of datavalues within the unique values vector to determine whether the datacell includes at least one data record that meets the search criteriafor at least the first data value; and condition the search of the datarecords of the data cell on a determination, via the comparison with thefirst hash values vector and the comparison with the unique valuesvector that the data cell that includes at least one data record thatmeets the search criteria.

The processor may be caused to perform operations including: parse thequery instructions to determine whether the query instructions includetask instructions for the performance of a task with data retrieved fromone or more data records identified as meeting the search criteria; andin response to a determination that the query instructions do includetask instructions for the performance of a task, perform operationsincluding: execute the instructions to perform the task at leastpartially in parallel with at least one other node device of themultiple node devices; and generate the results data to include resultsof the performance of the task as the indication that the super cellincludes at least one data record that meets the search criteria.

The processor may be caused to perform operations including: store, atthe first node device, a first super cell index corresponding to thefirst super cell, wherein the first super cell index may include anindication of a range of values stored within the first data fieldwithin the multiple data cells of the first super cell; in response tothe receipt of the query instructions, compare the at least one datavalue of the search criteria to the range of values indicated in thefirst super cell index to determine whether the first super cellincludes at least one data record within at least one data cell of thefirst super cell that meets the search criteria; and condition theperformance of the operations of the search for each data cell withinthe first super cell on a determination that the first super cell doesinclude at least one data record within at least one data cell of thefirst super cell that meets the search criteria.

The first node device may include a controller, and the controller mayinclude a controller processor and a controller storage to store otherinstructions that, when executed by the controller processor, cause thecontroller processor to perform operations to serve as the controldevice. The operations may include: receive, at the first node deviceand from a second node device of the multiple node devices, a secondsuper cell index corresponding to a second super cell stored by thesecond node device, wherein the second super cell index may include anindication of a range of values stored within the first data fieldwithin the at least one data cell of the second super cell; in responseto the receipt of the query instructions, compare the data value to therange of values indicated in the second super cell index to determinewhether the second super cell includes at least one data record withinat least one data cell of the second super cell that meets the searchcriteria; and in response to a determination that the second super cellincludes at least one data record that meets the search criteria,transmit the query instructions to the second node device to enable thesecond node device to perform the search within the at least one datacell of the second super cell.

The processor may be caused to receive, at the first node device, thefirst super cell from a data file maintained by at least one datadevice. The processor may also be caused to index, at the first nodedevice, and at least partially in parallel with other node devices ofthe multiple node devices, the multiple data records within each datacell of the multiple data cells by the first data field of the set offields in a single read pass through each data cell of the multiple datacells, wherein for each data record within the data cell, the processoris caused to perform operations including: retrieve a data value fromthe first data field; determine, based on the data value retrieved fromthe first data field, whether the first data field of the data recordstores a unique data value that has not yet retrieved by the processorfrom the first data field of any data record of the data cell; and inresponse to a determination that the first data field of the data recordstores a unique data value, add an identifier of the data record to afirst unique values index, wherein identifiers of data records withinthe first unique values index are ordered based on an ordering of thecorresponding unique data values in the first data field that isselected to enable use of the first unique values index to perform thesearch of the data records of the data cell. The processor may befurther caused to generate, within a super cell index corresponding tothe super cell, an indication of a range of the data values of the firstdata field within the data records of the data cell to enable use of thesuper cell index to determine whether the at least one data value of thesearch criteria is present within the first data field of any datarecord of the data cell.

The processor is caused to perform operations including: receive, at thefirst node device, and at least partially in parallel with other nodedevices of the multiple node devices, a stream of data values of thefirst super cell from at least one data device; generate, at the firstnode device, at least partially in parallel with other node devices ofthe multiple node devices, and according to at least one rule, themultiple data cells of the first super cell from the stream of datavalues, wherein the at least one rule is selected from group consistingof a rule selected from a group consisting of a minimum data size for adata cell, a maximum data size for a data cell, a minimum quantity ofdata records for a data cell, a maximum quantity of data records for adata cell, and a specification of the set of data fields; and index, atthe first node device, and at least partially in parallel with othernode devices of the multiple node devices, the multiple data recordswithin each data cell of the multiple data cells of the first supercell.

A computer-implemented method includes storing, at a first node deviceof multiple node devices, a first super cell of multiple super cellsinto which a data set is divided from a data file maintained by at leastone data device, wherein: the multiple super cells are distributed amongthe multiple node devices; each super cell comprises multiple datacells; each data cell of the multiple data cells comprises multiple datarecords; and each data record of the multiple data records comprises aset of data fields at which data values of the data set are stored. Themethod may also include storing, for each data cell within the firstsuper cell, a cell index that corresponds to the data cell, wherein thecell index comprises a first hash values vector that corresponds to afirst data field of the set of data fields, and that comprises hashvalues generated from each unique value among the data values storedwithin the first data field; and receiving, at the first node device,from a control device, and at least partially in parallel with othernode devices of the multiple node devices, query instructions specifyingsearch criteria of a search to be performed of the data set for datarecords that meet the search criteria, wherein the search criteriacomprises at least one data value to be searched for within the firstdata field. The method may also include, in response to the receipt ofthe query instructions, and for each data cell within the first supercell, performing operations of the search, the operations including:generating a first hash value from a first data value of the at leastone data value of the search criteria; comparing the first hash value tothe hash values within the first hash values vector in the correspondingcell index to determine whether the data cell includes at least one datarecord that meets the search criteria for at least the first data value;and in response to a determination that the data cell includes at leastone data record that meets the search criteria, searching the datarecords of the data cell to identify one or more data records that meetthe search criteria. The method may further include, in response toidentifying at least one data record within at least one data cell ofthe first super cell that meets the search criteria for at least thefirst data value, performing operations including generating resultsdata indicative of the first super cell including at least one datarecord that meets the search criteria for at least the first data value,and providing the results data to the control device.

Each of the cell indexes corresponding to the data cells within thefirst super cell may include a second hash values vector thatcorresponds to a second data field of the set of data fields, whereinthe second hash values vector comprises hash values generated from eachunique value among the data values stored within the second data field.The method may include: in response to identifying at least one datarecord within at least one data cell of the first super cell that meetsthe search criteria for at least the first data value, and for each datacell within the at least one data cell, the performing operations of thesearch, the operations including: generating a second hash value from asecond data value of the at least one data value of the search criteria;comparing the second hash value to the hash values within the secondhash values vector in the corresponding cell index to determine whetherthe data cell includes at least one data record that meets the searchcriteria for at least the first data value and the second data value;and in response to a determination that the data cell includes at leastone data record that meets the search criteria for at least the firstdata value and the second data value, searching the data records of thedata cell to identify one or more data records that meet the searchcriteria for at least the first data value and the second data value.The method may also include conditioning the generation and transmissionof results on identification of at least one data record within at leastone data cell of the first super cell that meets the search criteria forthe first data value and the second data value.

The method may include performing the search corresponding to the firstdata field on a first thread of execution, and performing the searchcorresponding to the second data field on a second thread of executionat least partially in parallel with the performance of the search on thefirst thread. The method may also include allocating a separateprocessor core of a processor to each of the first and second threads ofexecution.

Each of the cell indexes corresponding to the data cells within thefirst super cell may include a unique values vector that corresponds tothe first data field, wherein the unique values vector comprises asingle instance of each data values present within the first data fieldamong the data records of the corresponding data cell, wherein thesingle instances of each data value are sorted by value. The method mayinclude, in response to identifying at least one data record within atleast one data cell of the first super cell that meets the searchcriteria for at least the first data value, and for each data cellwithin the at least one data cell, the perform operations of the search,the operations including: comparing the first data value of the at leastone data value of the search criteria to the single instances of datavalues within the unique values vector to determine whether the datacell includes at least one data record that meets the search criteriafor at least the first data value; and conditioning the search of thedata records of the data cell on a determination, via the comparisonwith the first hash values vector and the comparison with the uniquevalues vector that the data cell that includes at least one data recordthat meets the search criteria.

The method may include: parsing the query instructions to determinewhether the query instructions include task instructions for theperformance of a task with data retrieved from one or more data recordsidentified as meeting the search criteria; and in response to adetermination that the query instructions do include task instructionsfor the performance of a task, performing operations including executingthe instructions to perform the task at least partially in parallel withat least one other node device of the multiple node devices, andgenerating the results data to include results of the performance of thetask as the indication that the super cell includes at least one datarecord that meets the search criteria.

The method may include: storing, at the first node device, a first supercell index corresponding to the first super cell, wherein the firstsuper cell index comprises an indication of a range of values storedwithin the first data field within the multiple data cells of the firstsuper cell; in response to the receipt of the query instructions,comparing the at least one data value of the search criteria to therange of values indicated in the first super cell index to determinewhether the first super cell includes at least one data record within atleast one data cell of the first super cell that meets the searchcriteria; and conditioning the performance of the operations of thesearch for each data cell within the first super cell on a determinationthat the first super cell does include at least one data record withinat least one data cell of the first super cell that meets the searchcriteria.

The first node device may include a controller that comprises acontroller processor and a controller storage to store otherinstructions that, when executed by the controller processor, cause thecontroller processor to perform operations to serve as the controldevice. the operations may include: receiving, at the first node deviceand from a second node device of the multiple node devices, a secondsuper cell index corresponding to a second super cell stored by thesecond node device, wherein the second super cell index comprises anindication of a range of values stored within the first data fieldwithin the at least one data cell of the second super cell; in responseto the receipt of the query instructions, comparing the data value tothe range of values indicated in the second super cell index todetermine whether the second super cell includes at least one datarecord within at least one data cell of the second super cell that meetsthe search criteria; and in response to a determination that the secondsuper cell includes at least one data record that meets the searchcriteria, transmitting the query instructions to the second node deviceto enable the second node device to perform the search within the atleast one data cell of the second super cell.

The method may include receiving, at the first node device, the firstsuper cell from a data file maintained by at least one data device. Themethod may also include indexing, at the first node device, and at leastpartially in parallel with other node devices of the multiple nodedevices, the multiple data records within each data cell of the multipledata cells by the first data field of the set of fields in a single readpass through each data cell of the multiple data cells, wherein for eachdata record within the data cell, the method may include: retrieving adata value from the first data field; determining, based on the datavalue retrieved from the first data field, whether the first data fieldof the data record stores a unique data value that has not yet retrievedby a processor of the first node device from the first data field of anydata record of the data cell; and in response to a determination thatthe first data field of the data record stores a unique data value,adding an identifier of the data record to a first unique values index,wherein identifiers of data records within the first unique values indexare ordered based on an ordering of the corresponding unique data valuesin the first data field that is selected to enable use of the firstunique values index to perform the search of the data records of thedata cell. The method may include generating, within a super cell indexcorresponding to the super cell, an indication of a range of the datavalues of the first data field within the data records of the data cellto enable use of the super cell index to determine whether the at leastone data value of the search criteria is present within the first datafield of any data record of the data cell.

The method may include: receiving, at the first node device, and atleast partially in parallel with other node devices of the multiple nodedevices, a stream of data values of the first super cell from at leastone data device; generating, at the first node device, at leastpartially in parallel with other node devices of the multiple nodedevices, and according to at least one rule, the multiple data cells ofthe first super cell from the stream of data values, wherein the atleast one rule is selected from group consisting of a rule selected froma group consisting of a minimum data size for a data cell, a maximumdata size for a data cell, a minimum quantity of data records for a datacell, a maximum quantity of data records for a data cell, and aspecification of the set of data fields; and indexing, at the first nodedevice, and at least partially in parallel with other node devices ofthe multiple node devices, the multiple data records within each datacell of the multiple data cells of the first super cell.

The foregoing, together with other features and embodiments, will becomemore apparent upon referring to the following specification, claims, andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is described in conjunction with the appendedfigures:

FIG. 1 illustrates a block diagram that provides an illustration of thehardware components of a computing system, according to some embodimentsof the present technology.

FIG. 2 illustrates an example network including an example set ofdevices communicating with each other over an exchange system and via anetwork, according to some embodiments of the present technology.

FIG. 3 illustrates a representation of a conceptual model of acommunications protocol system, according to some embodiments of thepresent technology.

FIG. 4 illustrates a communications grid computing system including avariety of control and worker nodes, according to some embodiments ofthe present technology.

FIG. 5 illustrates a flow chart showing an example process for adjustinga communications grid or a work project in a communications grid after afailure of a node, according to some embodiments of the presenttechnology.

FIG. 6 illustrates a portion of a communications grid computing systemincluding a control node and a worker node, according to someembodiments of the present technology.

FIG. 7 illustrates a flow chart showing an example process for executinga data analysis or processing project, according to some embodiments ofthe present technology.

FIG. 8 illustrates a block diagram including components of an EventStream Processing Engine (ESPE), according to embodiments of the presenttechnology.

FIG. 9 illustrates a flow chart showing an example process includingoperations performed by an event stream processing engine, according tosome embodiments of the present technology.

FIG. 10 illustrates an ESP system interfacing between a publishingdevice and multiple event subscribing devices, according to embodimentsof the present technology.

FIG. 11 illustrates a flow chart showing an example process ofgenerating and using a machine-learning model according to some aspects.

FIG. 12 illustrates an example machine-learning model based on a neuralnetwork.

FIGS. 13A and 13B each illustrate an example embodiment of a distributedprocessing system.

FIG. 14A illustrates an example embodiment of distribution of portionsof a data set among node devices of a grid of node devices.

FIG. 14B illustrates an example embodiment of organization of data andindexes of the data set of FIG. 14A within a data file exchanged with agrid of node devices.

FIG. 14C illustrates an example embodiment of organization of datavalues within a portion of a data set stored by a node device.

FIG. 15A illustrates an example of assembling, indexing, searching andperforming operations with a data set distributed among node devices ofa grid of node devices.

FIG. 15B illustrates an example of use of multithreaded processing toperform the operations of FIG. 15A.

FIG. 16A illustrates an example of assembling and indexing a portion ofa data set within a node device.

FIG. 16B illustrates an alternate example of assembling and indexing aportion of another data set within a node device.

FIGS. 17A, 17B and 17C, taken together, illustrate an example ofassembling and indexing the portion of the data set of FIG. 16A within anode device.

FIG. 18 illustrates an alternate example of assembling and indexing aportion of a data set within a node device.

FIGS. 19A, 19B and 19C, together, illustrate another alternate exampleof assembling and indexing a portion of a data set within a node device.

FIGS. 20A, 20B, 20C, 20D, 20E and 20F, taken together, illustrate anexample of searching within a portion of the data set of FIG. 16A withina node device.

FIG. 21 illustrates an alternate example of searching within a portionof the data set OF FIGS. 19A-C within a node device.

FIG. 22 illustrates an example of executing task instructions to performoperations with identified data records of a portion of a data setwithin a node device.

FIGS. 23A and 23B, taken together, illustrate an example embodiment of alogic flow of generating and using an data set index to search within adata set.

FIGS. 24A and 24B, taken together, illustrate an example embodiment of alogic flow of assigning portions of a data set to node devices andcontrolling performance of a search.

FIGS. 25A and 25B, taken together, illustrate an example embodiment of alogic flow of generating a portion of a data set index within a nodedevice.

FIGS. 26A, 26B, 26C, 26D, 26E and 26F, taken together, illustrates anexample embodiment of a logic flow of performing a search of a search.

DETAILED DESCRIPTION

Various embodiments described herein are generally directed tointer-device coordination to improve distributed indexing in whichparallel processing is used to access data of a data set. The data ofthe data set may be divided into multiple super cells, and the datawithin each super cell may be further divided into multiple data cells.Within each data cell, the data may be organized into a set of datarecords that each include an identical set of data fields filled withdata values. For each super cell, a set of indexes may be generated bywhich the data within the super cell may be more speedily accessed,including a super cell index corresponding to the entirety of the supercell and one or more cell indexes that each correspond to one of thedata cells within the super cell. Within each cell index, the datarecords within the corresponding data cell may be indexed by the datavalues found to be present within a selected subset of the data fields.For each selected data field, a unique values index within each cellindex may indicate each of the data values that are present within thedata field of at least one of the data records within the correspondingdata cell, and one or more duplicate value indexes may identify the datarecords that are found to contain a duplicate of any of the data valuesidentified in the unique value index. Additionally, within each cellindex, and for each of the selected data fields, the cell may beindications of the highest and lowest data values found to be present.Also, within each super cell index, the highest and lowest data valuesmay be indicated for each selected data field across the one or moredata cells within the corresponding super cell.

After being generated, the super cell indexes and cell indexes may thenbe used to increase the efficiency with which data records across theentirety of the data set may be searched in response to a query thatincludes search instructions that specify one or more discrete datavalues and/or ranges of data values within one or more specified datafields as search criteria. More specifically, in response to such aquery, the indications of highest and lowest data values within thesuper cell indexes for the specified one or more data fields may beanalyzed to identify one or more candidate super cells of the data setthat may have data records that meet the search criteria. Then, for eachcandidate super cell, the indications of highest and lowest data valueswithin the cell indexes for the specified one or more data fields may beanalyzed to identify one or more candidate data cells that may have datarecords that meet the search criteria. Following such narrowing down tocandidate data cells, a search for data records that meet the searchcriteria may then be performed within each candidate data cell.

The data of a data set may be any of a variety of types of data (e.g.,societal statistics data, business operations data, raw data fromsensors of large scale experiments, financial data, medical treatmentanalysis data, data from geological or meteorological instruments,streams of data collected from Internet-attached appliances, etc.). Thesize of the data set may be sufficiently large that accessing and/orprocessing the data set using a single processing device may be deemedhighly impractical. Indeed, it may be that the data set also changesfrequently enough over time (e.g., is updated hourly, daily, weekly,etc.) such that the length of time required to access and/or process thedata set using a single processing device would yield results that wouldalready be out of date before such operations could be completed. Thus,it may be deemed highly desirable to access and/or process the data setin a distributed and at least partially parallel manner using numerousprocessor cores able to support numerous threads of execution within asingle device, or a group of interconnected devices (sometimes referredto as a “grid” of devices) that each include one or more processors thatmay each include multiple processor cores.

Therefore, the data set may be distributed across multiple node devicesthat may each store one or more complete super cells. In someembodiments, such storage of the data set by multiple node devices maybe as part of persistently storing the data set for local access to themultiple node devices to enable the data set to remain readily availablefor processing operations to be performed in a distributed parallelmanner among the multiple node devices. In other embodiments, the dataset may be temporarily provided to the multiple node devices for suchprocessing from one or more data devices at which the data set may bepersistently stored. In still other embodiments, the data set may bebuilt up within the multiple node devices over a period of time, and/orrecurringly updated within the multiple node devices, from data valuesreceived at the multiple node devices from one or more data devices.

It is envisioned that the data set may be of sufficient size as tonecessitate being divided into a great many super cells, and to furthernecessitate each super cell being divided into a great many data cells.Also, each data cell may contain a great many data records within whichdata values may be organized to occupy a great many data fields. Inembodiments in which the data is organized into a two-dimensional array(e.g., organized into an array of rows and columns), each of the datarecords may be implemented as a row of a great many rows distributedamong the data cells and super cells, and each of the data fields maycorrespond to a column of a great many columns present within each row.To increase the efficiency with which data may be searched and accessedwithin the data set, super cell indexes and cell indexes may bedistributed among the multiple node devices in a manner in which eachsuper cell index may be stored within a node device alongside itscorresponding super cell and each cell index may be stored alongside itscorresponding data cell. At least in part to avoid instances in whichany one super cell index may be split among, and otherwise shared among,more than one node device, a requirement may be imposed that no supercell is permitted to be split among two or more node devices such thatthe entirety of each super cell must be stored within a single nodedevice.

As part of keeping each super cell index with its corresponding supercell and each cell index with its corresponding data cell, each transferof a super cell of the data set between devices may be accompanied by atransfer of its corresponding super cell index and the cell indexes thatcorrespond to the data cells within the super cell. Therefore, inembodiments in which the data set is persisted within one or more datadevices, and is temporarily provided to the multiple node devices, eachnode device may receive one or more whole super cells along with acorresponding one or more super cell indexes and the cell indexes thatcorrespond to each of the data cells within the received one or moresuper cells. Alternatively, in embodiments in which the data set may beprovided to the multiple node devices without any accompanying supercell indexes or cell indexes, including embodiments in which themultiple node devices assemble and/or update the data set from datavalues received over time, the multiple node devices may generate thecorresponding super cell indexes and cell indexes.

Although it may be possible to generate indexes that include all datafields, it is envisioned that the data values of a relatively smallsubset of the data fields may be used in the search criteria specifiedin queries that include instructions for searches within the data set.In some embodiments, the subset of data fields used in such searchcriteria may be well known and may be explicitly specified by rulesstored as rules data. In other embodiments, such rules data mayalternatively or additionally specify rules and/or heuristic algorithmsto identify the subset of data fields based on a history of previousqueries and/or other factors. Regardless of the manner in which thesubset of data fields is specified and/or identified, the generation ofcell indexes and super cell indexes by the multiple node devices may belimited in scope to such a subset of the data fields of a data set.

The generation of the indexes from the data set may begin with thegeneration of each cell index from the data values found within thesubset of data fields of its corresponding data cell. It may be deemeddesirable to perform the generation of what may be numerous cell indexeswithin each node device across numerous threads of execution to bringabout at least partially parallel generation of cell indexes within nodedevices as well as among multiple node devices. More specifically,within each node device, the generation of each cell index may beperformed as a separate process with such processes distributed amongmultiple threads of execution to the extent supported by availableprocessor cores of the processor(s) of each node device.

In generating each cell index, the data values in the subset of datafields within the data records of the corresponding cell are retrieved.As another measure to increase the efficiency with which each cell indexis generated, such retrieval of data values for multiple data fields maybe performed in a single read pass through the data records of thecorresponding data cell. For each cell index that is to be generated, aseparate binary tree may be generated for each data field of the subsetof data fields within the data records of the single corresponding datacell. As each such binary tree is generated, the various data valuesthat are identified as present within the corresponding data field ofthe subset of data fields may be sorted in accordance with one or morerules that may be selected based on the type of data within the datafield. Also, as each such binary tree is generated, the binary tree maybe used to identify any duplicates of any of the data values identifiedas present within the corresponding data field. As duplicates among datavalues within a data field are identified, tables and/or other datastructures may be generated that correlate each instance of a duplicatedata value to an identifier of the data record in which the duplicatedata value is present.

With a binary tree for each data field of the subset of data fields sogenerated for a single data cell, a separate index of unique values maybe generated within the corresponding cell index from each binary tree.Within each unique values index, the identifiers of the data recordswithin which each unique data value was identified may be arranged in anorder that corresponds to the order into which the unique data valueswere sorted during generation of the corresponding binary tree.Generation of each unique values index may entail an in-order traversalof the corresponding binary tree. Each unique values index may alsoinclude and/or be accompanied by a count of the unique data values,indications of highest and lowest unique data values that wereidentified within the corresponding data field of the data records ofthe corresponding data cell, and/or highest and lowest hash valuesgenerated from the unique data values.

In some embodiments, one or more of the unique values indexes mayinclude and/or be accompanied by a vector or other data structure of theunique data values also arranged in the order that corresponds to theorder into which the unique data values were sorted during generation ofthe corresponding binary tree. Alternatively or additionally, one ormore of the unique values indexes may include and/or be accompanied by avector or other data structure of ordered hash values derived from theunique data values. In some of such embodiments, the determination ofwhether a vector of unique values and/or whether a vector of hash valuesis generated for each unique values index within a cell index may bebased on the identified data type of the data values within thecorresponding data field. By way of example for numeric values and/orfixed length text strings, a vector of the unique data values may begenerated. However, data of variable data size and/or of large datasize, such as audio and/or video streams or text strings of variablelength, a vector of hash values derived from each of the data values maybe generated.

Where there are one or more tables or other data structures that havebeen generated to indicate duplicates of data values identified withinone or more data fields, such one or more tables or other datastructures may be used to generate one or more indexes of duplicatevalues within the cell index. In some embodiments, each duplicate valueindex may be separately generated for a single duplicated data value,and may specify the one or more data records within the correspondingdata cell in which each duplicate of the data value is present. Eachsuch duplicate value index may also include a count of the duplicates ofthe data value within the corresponding data field of the data recordsof the corresponding data cell.

Following the generation of a cell index for each data cell of a supercell, the generation of indexes for the super cell may continue with thegeneration of a corresponding super cell index. As with the generationof the cell indexes, it may be deemed desirable to perform thegeneration of what may be numerous super cell indexes within each nodedevice across numerous threads of execution to bring about at leastpartially parallel generation of super cell indexes within node devicesas well as among multiple node devices. More specifically, within eachnode device, the generation of each super cell index may be performed asa separate process, and distributed among multiple threads of executionto the extent supported by available processor cores of the processor(s)of each node device.

Each super cell index may be generated to include indications of thehighest and lowest data values identified within each data field of thesubset of data fields across the data cells within the correspondingsuper cell. Additionally, each super cell index may be generated toinclude indications of the highest lowest hash values among the highestand lowest hash values generated from unique data values for each datacell. In generating each indication of the highest and lowest datavalues for a data field within the super cell index, the highest andlowest data values for the data field within each of the cell indexesfor one of the data cells of the corresponding super cell may beretrieved, and the highest and lowest among the retrieved data valuesmay be selected. More specifically, a binary tree may be generated usingthe highest and lowest values indicated within each cell index for adata field among the data cells of a super cell, and then the highestand lowest values for the data field within the data records throughoutthe super cell may be identified from the binary tree. A similarapproach may be used to generate each indication of the highest andlowest hash values. With a cell index having been generated for eachdata cell, and with a super cell index having been generated for eachsuper cell within a node device, the portion of a data set stored withinthe node device is indexed such that searches for data records withineach of the super cells may be performed more efficiently and quickly.

The multiple node devices may be controlled by a control device or acontroller incorporated into one of the multiple node devices. Such acontrol device or controller may control the manner in which the supercells of a data set may be distributed among the multiple node devices.More specifically, where the multiple node devices are provided the dataset in its entirety by one or more data devices, the control device orcontroller may determine which super cells of the data set are to beprovided to each of the node devices. Alternatively, where the data setis to be generated by the multiple node devices from data valuescollected by the multiple node devices over time from one or more datadevices, the control device or controller may determine the quantityand/or size of each super cell to be generated within each node device.Such determinations by the control device or controller may be based onindications of currently available processing, storage, network and/orother resources that may be recurringly provided to the control deviceor controller by each of the node devices. Alternatively oradditionally, such determinations may be based on rules and/or analgorithm for achieving a distribution of the data set among themultiple node devices that incorporates some degree of redundancy toavoid loss of data as a result of a failure occurring within one or moreof the node devices.

The control device or controller may serve as a receiver of queries fromrequesting devices, where each query may include instructions to performa search within the data set for data record(s) meeting specified searchcriteria. In some embodiments, the control device or controller maysimply relay the query to each node device of the multiple node devices.Alternatively, the control device or controller may select a subset ofthe multiple node devices to relay the query to based on thedistribution of super cells and redundant copies of super cells amongthe multiple node devices to at least reduce instances of having morethan one node device engaged in determining whether the same super cellis a candidate super cell. Regardless of whether the control device orcontroller relays the query to all of the multiple node devices, or toonly to a selected subset, each node device that receives the query maythen perform various operations to identify any candidate super cellsthat may have one or more data cells that may have one or more datarecords that meet specified search criteria. Each node device thatstores at least one candidate super cell may then perform operations toidentify any candidate data cells within each any candidate super cellthat may have one or more data records that meet the specified criteria.

In other embodiments, it may be the control device or controller thatperforms the operations needed to determine which super cells arecandidate super cells as part of reducing the number of node devices towhich the query may be relayed. In support of this, each of the nodedevices of the multiple node devices may provide each of the super cellindexes that it generates and/or stores to the control device orcontroller. Upon receiving a query, the control device or controller maythen use the set of super cell indexes provided by the multiple nodedevices to identify the one or more candidate super cells that may eachinclude one or more data cells that may each include one or more datarecords that meet the search criteria. The control device or controllermay then relay the query to a subset of the multiple node devices thatstore the candidate super cells. Each node device that receives thequery may then perform operations to identify any candidate data cellswithin any candidate super cell that may have one or more data recordsthat meet the specified criteria. The control device or controller mayfurther reduce the subset of the multiple node devices based on thedistribution of super cells and redundant copies of super cells amongthe multiple node devices to at least reduce instances of having morethan one node device engaged in determining whether the same candidatesuper cell is a candidate super cell.

Regardless of whether it is the control device or controller thatperforms operations to identify candidate super cells, or it is at leasta subset of the node devices that performs such operations, identifyingcandidate super cells may entail comparing the one or more discrete datavalues and/or ranges of data values specified as the search criteria inthe query to the range(s) of data values defined in each super cellindex with indications of highest and lowest data values therein. By wayof example, if the search criteria of a query indicates that a specificdata value or a data value within a specific range of data values isrequired to be present in a specific data field of any data record thatmeets the search criteria, and if such a data value or range of datavalues so specified by the query falls entirely outside the range ofdata values defined by the highest and lowest data values indicated in asuper cell index for the specific data field, then the correspondingsuper cell cannot be a candidate super cell. Further, in embodiments inwhich the super cell indexes include an indication of a range of hashvalues defined by highest and lowest hash values for the specific datafield, and in which the query specifies one or more discrete data valuesas the search criteria, a hash value may be generated from each suchspecific data value, and each such hash value may be compared to therange of hash values indicated in the super cell index as an additionaltest of whether the corresponding super cell can be a candidate supercell. Similarly, within each node device that stores one or morecandidate super cells, identifying candidate data cells within eachcandidate super cell may entail a similar comparison of the data valuesand/or ranges of data values specified as the search criteria in thequery to the range(s) of data values defined in each cell index withindications of highest and lowest data values therein. Further, suchidentification of candidate data cells may additionally entail thecomparison of hash value(s) generated from specific data value(s)specified as the search criteria in the query to the range(s) of hashvalues that may be defined in each cell index with indications ofhighest and lowest hash values therein.

As with the aforedescribed generation of super cell indexes and cellindexes, it may be deemed desirable to perform the operations toidentify candidate super cells and/or candidate data cells acrossnumerous threads of execution to bring about at least partially parallelperformances of operations to identify candidate super cells and/orcandidate data cells. Therefore, where the identification of candidatesuper cells is performed within the control device or controller, suchoperations may be performed in a separate process for each super cellindex, and distributed among multiple threads supported by availableprocessor cores of the processor(s) of the control device or controller.Alternatively, where the identification of candidate super cells isperformed by at least a subset of the multiple node devices, suchoperations may be performed in a separate process for each super cellindex, and distributed among multiple threads supported by availableprocessor cores of the processor(s) of each of the corresponding nodedevices. Similarly, within each of the node devices that are identifiedas storing at least one candidate super cell, the operations to identifycandidate data cells may be performed in a separate process for eachcell index corresponding to a data cell of a candidate super cell, andeach such separate process may be distributed among multiple threadssupported by available processor cores of the processor(s) of each ofthe corresponding node devices. Each node device to which the query isrelayed, and in which no candidate data cells have been identified, maytransmit an indication to the control device or controller that no datarecords meeting the search criteria have been found, and may then ceaseto take any further action in response to receiving the query.

In each node device in which at least one candidate data cell has beenidentified, the data records within each such candidate data may then besearched to identify one or more data records that meet the searchcriteria. In embodiments in which the unique values index for a datafield within the cell index of a candidate data cell does not include oris not accompanied by a vector or other data structure of either theunique data values or hash values generated from the unique data values,the identifiers of data records that include each unique data value,and/or one or more duplicate value indexes, may be used to guide asearch through the data records within the candidate data cell. However,in embodiments in which the unique values index for a data field withinthe cell index of a candidate data cell does include or is accompaniedby a vector or other data structure of either the unique data values orhash values generated therefrom, such a vector or data structure may beused to determine whether there are any data records in the candidatedata cell that meet the search criteria. If it is determined that thereis at least one such data record in embodiments in which the vector orother data structure is of hash values generated from the unique datavalues, then a search among the data records to retrieve the one or moredata records that meet the search criteria may be performed. Incontrast, if it is determined that there is at least one such datarecord in embodiments in which the vector or other data structure is ofthe unique data values, then the need to perform such a search may beobviated as a result of each of the unique data values within the vectoror other data structure already being correlated by the unique valuesindex to the data record in which it is present.

Any of a variety of search algorithms may be employed in performing eachsearch associated with each data field. Alternatively or additionally, acombination of search algorithms may be used in which an initial searchalgorithm is used to identify a first data record that meets the searchcriteria for a specific data field, and then a different searchalgorithm may be used to search for any more data records that meet thesearch criteria for the specific data field. Where the search criteriainvolves more than one data field, for each candidate data cell, ananalysis may be made of the counts of unique data values and ofduplicates of each data value to determine the relative degrees ofcardinality of the data values among the data records within thecandidate data cell for each of the data fields involved in the searchcriteria. Then, for each candidate data cell, a determination based onrelative cardinality may be made of the order in which the more than onedata fields are to be searched as part of searching for data recordsthat meet the search criteria. Each node device to which the query isrelayed, and in at least one candidate data cell was identified, but inwhich no data records were found that meet the search criteria, maytransmit an indication to the control device or controller that no datarecords meeting the search criteria have been found, and may then ceaseto take any further action in response to receiving the query.

In each node device in which at least one data record is identified thatmeets the search criteria, any of a variety of actions may be taken inresponse to having identified at least one data record that meets thesearch criteria specified in a query, and the particular actions takenmay depend on further instructions included in the query. By way ofexample, where the query includes a request for an indication of whichdata records meet the search criteria, each node device in which atleast one of such data records is found may transmit a bit field orother data structure to the control device or controller that identifiesthe one or more data records stored within the node device that meet thesearch criteria. Where the query includes a request for one or more datavalues to be retrieved from any data record that meets the searchcriteria, each node device in which at least one of such data records isfound may transmit the requested data values from each such data record,or may transmit the entirety of each such data record to the controldevice or controller. Where the query includes further instructions toperform one or more processing operations with data values retrievedfrom any data records that meet the search criteria, each node device inwhich at least one of such data records is found may perform thespecified processing operations on such specified data values and maytransmit an indication of the results of the specified processingoperations to the control device or controller.

As with the aforedescribed performances of operations to identifycandidate super cells and/or candidate data cells, it may be deemeddesirable to perform the searches of data records within each candidatedata cell, as well as any specified processing operations with datavalues retrieved from data records that meet the search criteria, acrossnumerous threads of execution. Therefore, within each of the nodedevices that are identified as storing at least one candidate data cell,the operations to search through the data records within candidate datacells may be performed in a separate process for each candidate datacell, and each such separate process may be distributed among multiplethreads supported by available processor cores of the processor(s) ofeach of the corresponding node devices. Also, within each of the nodedevices that are identified as storing at least one candidate data cellin which at least one data record is identified that meets the searchcriteria, any processing operations that are specified in the receivedquery to be performed with data values from such data records may beperformed in a separate process for each such candidate data cell, andeach such separate process may also be distributed among multiplethreads supported by available processor cores of the processor(s) ofeach of the corresponding node devices.

It should be noted that, although the use of binary trees is explicitlydiscussed herein in some detail for distinguishing between unique andduplicate values, identifying highest and lowest values, and sortingvalues, other approaches may be employed in performing one or more ofthese operations, either in place of using binary trees or incombination with binary trees. By way of example, skip lists may be usedto perform one or more of these functions. Further, although the use ofbinary searching techniques is explicitly discussed herein in somedetail to perform searches of data cells to identify data records thatmeet search criteria, other approaches may be employed in performingsuch searches, either in place of binary search techniques or incombination with binary search techniques. By way of example, skip listsmay be used in combination with binary searching techniques.

With general reference to notations and nomenclature used herein,portions of the detailed description that follows may be presented interms of program procedures executed by a processor of a machine or ofmultiple networked machines. These procedural descriptions andrepresentations are used by those skilled in the art to most effectivelyconvey the substance of their work to others skilled in the art. Aprocedure is here, and generally, conceived to be a self-consistentsequence of operations leading to a desired result. These operations arethose requiring physical manipulations of physical quantities. Usually,though not necessarily, these quantities take the form of electrical,magnetic or optical communications capable of being stored, transferred,combined, compared, and otherwise manipulated. It proves convenient attimes, principally for reasons of common usage, to refer to what iscommunicated as bits, values, elements, symbols, characters, terms,numbers, or the like. It should be noted, however, that all of these andsimilar terms are to be associated with the appropriate physicalquantities and are merely convenient labels applied to those quantities.

Further, these manipulations are often referred to in terms, such asadding or comparing, which are commonly associated with mentaloperations performed by a human operator. However, no such capability ofa human operator is necessary, or desirable in most cases, in any of theoperations described herein that form part of one or more embodiments.Rather, these operations are machine operations. Useful machines forperforming operations of various embodiments include machinesselectively activated or configured by a routine stored within that iswritten in accordance with the teachings herein, and/or includeapparatus specially constructed for the required purpose. Variousembodiments also relate to apparatus or systems for performing theseoperations. These apparatus may be specially constructed for therequired purpose or may include a general purpose computer. The requiredstructure for a variety of these machines will appear from thedescription given.

Reference is now made to the drawings, wherein like reference numeralsare used to refer to like elements throughout. In the followingdescription, for purposes of explanation, numerous specific details areset forth in order to provide a thorough understanding thereof. It maybe evident, however, that the novel embodiments can be practiced withoutthese specific details. In other instances, well known structures anddevices are shown in block diagram form in order to facilitate adescription thereof. The intention is to cover all modifications,equivalents, and alternatives within the scope of the claims.

Systems depicted in some of the figures may be provided in variousconfigurations. In some embodiments, the systems may be configured as adistributed system where one or more components of the system aredistributed across one or more networks in a cloud computing systemand/or a fog computing system.

FIG. 1 is a block diagram that provides an illustration of the hardwarecomponents of a data transmission network 100, according to embodimentsof the present technology. Data transmission network 100 is aspecialized computer system that may be used for processing largeamounts of data where a large number of computer processing cycles arerequired.

Data transmission network 100 may also include computing environment114. Computing environment 114 may be a specialized computer or othermachine that processes the data received within the data transmissionnetwork 100. Data transmission network 100 also includes one or morenetwork devices 102. Network devices 102 may include client devices thatattempt to communicate with computing environment 114. For example,network devices 102 may send data to the computing environment 114 to beprocessed, may send signals to the computing environment 114 to controldifferent aspects of the computing environment or the data it isprocessing, among other reasons. Network devices 102 may interact withthe computing environment 114 through a number of ways, such as, forexample, over one or more networks 108. As shown in FIG. 1, computingenvironment 114 may include one or more other systems. For example,computing environment 114 may include a database system 118 and/or acommunications grid 120.

In other embodiments, network devices may provide a large amount ofdata, either all at once or streaming over a period of time (e.g., usingevent stream processing (ESP), described further with respect to FIGS.8-10), to the computing environment 114 via networks 108. For example,network devices 102 may include network computers, sensors, databases,or other devices that may transmit or otherwise provide data tocomputing environment 114. For example, network devices may includelocal area network devices, such as routers, hubs, switches, or othercomputer networking devices. These devices may provide a variety ofstored or generated data, such as network data or data specific to thenetwork devices themselves. Network devices may also include sensorsthat monitor their environment or other devices to collect dataregarding that environment or those devices, and such network devicesmay provide data they collect over time. Network devices may alsoinclude devices within the internet of things, such as devices within ahome automation network. Some of these devices may be referred to asedge devices, and may involve edge computing circuitry. Data may betransmitted by network devices directly to computing environment 114 orto network-attached data stores, such as network-attached data stores110 for storage so that the data may be retrieved later by the computingenvironment 114 or other portions of data transmission network 100.

Data transmission network 100 may also include one or morenetwork-attached data stores 110. Network-attached data stores 110 areused to store data to be processed by the computing environment 114 aswell as any intermediate or final data generated by the computing systemin non-volatile memory. However in certain embodiments, theconfiguration of the computing environment 114 allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory (e.g., disk). This can be useful in certain situations, such aswhen the computing environment 114 receives ad hoc queries from a userand when responses, which are generated by processing large amounts ofdata, need to be generated on-the-fly. In this non-limiting situation,the computing environment 114 may be configured to retain the processedinformation within memory so that responses can be generated for theuser at different levels of detail as well as allow a user tointeractively query against this information.

Network-attached data stores may store a variety of different types ofdata organized in a variety of different ways and from a variety ofdifferent sources. For example, network-attached data storage mayinclude storage other than primary storage located within computingenvironment 114 that is directly accessible by processors locatedtherein. Network-attached data storage may include secondary, tertiaryor auxiliary storage, such as large hard drives, servers, virtualmemory, among other types. Storage devices may include portable ornon-portable storage devices, optical storage devices, and various othermediums capable of storing, containing data. A machine-readable storagemedium or computer-readable storage medium may include a non-transitorymedium in which data can be stored and that does not include carrierwaves and/or transitory electronic signals. Examples of a non-transitorymedium may include, for example, a magnetic disk or tape, opticalstorage media such as compact disk or digital versatile disk, flashmemory, memory or memory devices. A computer-program product may includecode and/or machine-executable instructions that may represent aprocedure, a function, a subprogram, a program, a routine, a subroutine,a module, a software package, a class, or any combination ofinstructions, data structures, or program statements. A code segment maybe coupled to another code segment or a hardware circuit by passingand/or receiving information, data, arguments, parameters, or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, amongothers. Furthermore, the data stores may hold a variety of differenttypes of data. For example, network-attached data stores 110 may holdunstructured (e.g., raw) data, such as manufacturing data (e.g., adatabase containing records identifying products being manufactured withparameter data for each product, such as colors and models) or productsales databases (e.g., a database containing individual data recordsidentifying details of individual product sales).

The unstructured data may be presented to the computing environment 114in different forms such as a flat file or a conglomerate of datarecords, and may have data values and accompanying time stamps. Thecomputing environment 114 may be used to analyze the unstructured datain a variety of ways to determine the best way to structure (e.g.,hierarchically) that data, such that the structured data is tailored toa type of further analysis that a user wishes to perform on the data.For example, after being processed, the unstructured time stamped datamay be aggregated by time (e.g., into daily time period units) togenerate time series data and/or structured hierarchically according toone or more dimensions (e.g., parameters, attributes, and/or variables).For example, data may be stored in a hierarchical data structure, suchas a ROLAP OR MOLAP database, or may be stored in another tabular form,such as in a flat-hierarchy form.

Data transmission network 100 may also include one or more server farms106. Computing environment 114 may route select communications or datato the one or more sever farms 106 or one or more servers within theserver farms. Server farms 106 can be configured to provide informationin a predetermined manner. For example, server farms 106 may access datato transmit in response to a communication. Server farms 106 may beseparately housed from each other device within data transmissionnetwork 100, such as computing environment 114, and/or may be part of adevice or system.

Server farms 106 may host a variety of different types of dataprocessing as part of data transmission network 100. Server farms 106may receive a variety of different data from network devices, fromcomputing environment 114, from cloud network 116, or from othersources. The data may have been obtained or collected from one or moresensors, as inputs from a control database, or may have been received asinputs from an external system or device. Server farms 106 may assist inprocessing the data by turning raw data into processed data based on oneor more rules implemented by the server farms. For example, sensor datamay be analyzed to determine changes in an environment over time or inreal-time.

Data transmission network 100 may also include one or more cloudnetworks 116. Cloud network 116 may include a cloud infrastructuresystem that provides cloud services. In certain embodiments, servicesprovided by the cloud network 116 may include a host of services thatare made available to users of the cloud infrastructure system ondemand. Cloud network 116 is shown in FIG. 1 as being connected tocomputing environment 114 (and therefore having computing environment114 as its client or user), but cloud network 116 may be connected to orutilized by any of the devices in FIG. 1. Services provided by the cloudnetwork can dynamically scale to meet the needs of its users. The cloudnetwork 116 may include one or more computers, servers, and/or systems.In some embodiments, the computers, servers, and/or systems that make upthe cloud network 116 are different from the user's own on-premisescomputers, servers, and/or systems. For example, the cloud network 116may host an application, and a user may, via a communication networksuch as the Internet, on demand, order and use the application.

While each device, server and system in FIG. 1 is shown as a singledevice, it will be appreciated that multiple devices may instead beused. For example, a set of network devices can be used to transmitvarious communications from a single user, or remote server 140 mayinclude a server stack. As another example, data may be processed aspart of computing environment 114.

Each communication within data transmission network 100 (e.g., betweenclient devices, between servers 106 and computing environment 114 orbetween a server and a device) may occur over one or more networks 108.Networks 108 may include one or more of a variety of different types ofnetworks, including a wireless network, a wired network, or acombination of a wired and wireless network. Examples of suitablenetworks include the Internet, a personal area network, a local areanetwork (LAN), a wide area network (WAN), or a wireless local areanetwork (WLAN). A wireless network may include a wireless interface orcombination of wireless interfaces. As an example, a network in the oneor more networks 108 may include a short-range communication channel,such as a BLUETOOTH® communication channel or a BLUETOOTH® Low Energycommunication channel. A wired network may include a wired interface.The wired and/or wireless networks may be implemented using routers,access points, bridges, gateways, or the like, to connect devices in thenetwork 114, as will be further described with respect to FIG. 2. Theone or more networks 108 can be incorporated entirely within or caninclude an intranet, an extranet, or a combination thereof. In oneembodiment, communications between two or more systems and/or devicescan be achieved by a secure communications protocol, such as securesockets layer (SSL) or transport layer security (TLS). In addition, dataand/or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things(e.g., machines, devices, phones, sensors) can be connected to networksand the data from these things can be collected and processed within thethings and/or external to the things. For example, the IoT can includesensors in many different devices, and high value analytics can beapplied to identify hidden relationships and drive increasedefficiencies. This can apply to both big data analytics and real-time(e.g., ESP) analytics. This will be described further below with respectto FIG. 2.

As noted, computing environment 114 may include a communications grid120 and a transmission network database system 118. Communications grid120 may be a grid-based computing system for processing large amounts ofdata. The transmission network database system 118 may be for managing,storing, and retrieving large amounts of data that are distributed toand stored in the one or more network-attached data stores 110 or otherdata stores that reside at different locations within the transmissionnetwork database system 118. The compute nodes in the grid-basedcomputing system 120 and the transmission network database system 118may share the same processor hardware, such as processors that arelocated within computing environment 114.

FIG. 2 illustrates an example network including an example set ofdevices communicating with each other over an exchange system and via anetwork, according to embodiments of the present technology. As noted,each communication within data transmission network 100 may occur overone or more networks. System 200 includes a network device 204configured to communicate with a variety of types of client devices, forexample client devices 230, over a variety of types of communicationchannels.

As shown in FIG. 2, network device 204 can transmit a communication overa network (e.g., a cellular network via a base station 210). Thecommunication can be routed to another network device, such as networkdevices 205-209, via base station 210. The communication can also berouted to computing environment 214 via base station 210. For example,network device 204 may collect data either from its surroundingenvironment or from other network devices (such as network devices205-209) and transmit that data to computing environment 214.

Although network devices 204-209 are shown in FIG. 2 as a mobile phone,laptop computer, tablet computer, temperature sensor, motion sensor, andaudio sensor respectively, the network devices may be or include sensorsthat are sensitive to detecting aspects of their environment. Forexample, the network devices may include sensors such as water sensors,power sensors, electrical current sensors, chemical sensors, opticalsensors, pressure sensors, geographic or position sensors (e.g., GPS),velocity sensors, acceleration sensors, flow rate sensors, among others.Examples of characteristics that may be sensed include force, torque,load, strain, position, temperature, air pressure, fluid flow, chemicalproperties, resistance, electromagnetic fields, radiation, irradiance,proximity, acoustics, moisture, distance, speed, vibrations,acceleration, electrical potential, electrical current, among others.The sensors may be mounted to various components used as part of avariety of different types of systems (e.g., an oil drilling operation).The network devices may detect and record data related to theenvironment that it monitors, and transmit that data to computingenvironment 214.

As noted, one type of system that may include various sensors thatcollect data to be processed and/or transmitted to a computingenvironment according to certain embodiments includes an oil drillingsystem. For example, the one or more drilling operation sensors mayinclude surface sensors that measure a hook load, a fluid rate, atemperature and a density in and out of the wellbore, a standpipepressure, a surface torque, a rotation speed of a drill pipe, a rate ofpenetration, a mechanical specific energy, etc. and downhole sensorsthat measure a rotation speed of a bit, fluid densities, downholetorque, downhole vibration (axial, tangential, lateral), a weightapplied at a drill bit, an annular pressure, a differential pressure, anazimuth, an inclination, a dog leg severity, a measured depth, avertical depth, a downhole temperature, etc. Besides the raw datacollected directly by the sensors, other data may include parameterseither developed by the sensors or assigned to the system by a client orother controlling device. For example, one or more drilling operationcontrol parameters may control settings such as a mud motor speed toflow ratio, a bit diameter, a predicted formation top, seismic data,weather data, etc. Other data may be generated using physical modelssuch as an earth model, a weather model, a seismic model, a bottom holeassembly model, a well plan model, an annular friction model, etc. Inaddition to sensor and control settings, predicted outputs, of forexample, the rate of penetration, mechanical specific energy, hook load,flow in fluid rate, flow out fluid rate, pump pressure, surface torque,rotation speed of the drill pipe, annular pressure, annular frictionpressure, annular temperature, equivalent circulating density, etc. mayalso be stored in the data warehouse.

In another example, another type of system that may include varioussensors that collect data to be processed and/or transmitted to acomputing environment according to certain embodiments includes a homeautomation or similar automated network in a different environment, suchas an office space, school, public space, sports venue, or a variety ofother locations. Network devices in such an automated network mayinclude network devices that allow a user to access, control, and/orconfigure various home appliances located within the user's home (e.g.,a television, radio, light, fan, humidifier, sensor, microwave, iron,and/or the like), or outside of the user's home (e.g., exterior motionsensors, exterior lighting, garage door openers, sprinkler systems, orthe like). For example, network device 102 may include a home automationswitch that may be coupled with a home appliance. In another embodiment,a network device can allow a user to access, control, and/or configuredevices, such as office-related devices (e.g., copy machine, printer, orfax machine), audio and/or video related devices (e.g., a receiver, aspeaker, a projector, a DVD player, or a television), media-playbackdevices (e.g., a compact disc player, a CD player, or the like),computing devices (e.g., a home computer, a laptop computer, a tablet, apersonal digital assistant (PDA), a computing device, or a wearabledevice), lighting devices (e.g., a lamp or recessed lighting), devicesassociated with a security system, devices associated with an alarmsystem, devices that can be operated in an automobile (e.g., radiodevices, navigation devices), and/or the like. Data may be collectedfrom such various sensors in raw form, or data may be processed by thesensors to create parameters or other data either developed by thesensors based on the raw data or assigned to the system by a client orother controlling device.

In another example, another type of system that may include varioussensors that collect data to be processed and/or transmitted to acomputing environment according to certain embodiments includes a poweror energy grid. A variety of different network devices may be includedin an energy grid, such as various devices within one or more powerplants, energy farms (e.g., wind farm, solar farm, among others) energystorage facilities, factories, homes and businesses of consumers, amongothers. One or more of such devices may include one or more sensors thatdetect energy gain or loss, electrical input or output or loss, and avariety of other efficiencies. These sensors may collect data to informusers of how the energy grid, and individual devices within the grid,may be functioning and how they may be made more efficient.

Network device sensors may also perform processing on data it collectsbefore transmitting the data to the computing environment 114, or beforedeciding whether to transmit data to the computing environment 114. Forexample, network devices may determine whether data collected meetscertain rules, for example by comparing data or values calculated fromthe data and comparing that data to one or more thresholds. The networkdevice may use this data and/or comparisons to determine if the datashould be transmitted to the computing environment 214 for further useor processing.

Computing environment 214 may include machines 220 and 240. Althoughcomputing environment 214 is shown in FIG. 2 as having two machines, 220and 240, computing environment 214 may have only one machine or may havemore than two machines. The machines that make up computing environment214 may include specialized computers, servers, or other machines thatare configured to individually and/or collectively process large amountsof data. The computing environment 214 may also include storage devicesthat include one or more databases of structured data, such as dataorganized in one or more hierarchies, or unstructured data. Thedatabases may communicate with the processing devices within computingenvironment 214 to distribute data to them. Since network devices maytransmit data to computing environment 214, that data may be received bythe computing environment 214 and subsequently stored within thosestorage devices. Data used by computing environment 214 may also bestored in data stores 235, which may also be a part of or connected tocomputing environment 214.

Computing environment 214 can communicate with various devices via oneor more routers 225 or other inter-network or intra-network connectioncomponents. For example, computing environment 214 may communicate withdevices 230 via one or more routers 225. Computing environment 214 maycollect, analyze and/or store data from or pertaining to communications,client device operations, client rules, and/or user-associated actionsstored at one or more data stores 235. Such data may influencecommunication routing to the devices within computing environment 214,how data is stored or processed within computing environment 214, amongother actions.

Notably, various other devices can further be used to influencecommunication routing and/or processing between devices within computingenvironment 214 and with devices outside of computing environment 214.For example, as shown in FIG. 2, computing environment 214 may include aweb server 240. Thus, computing environment 214 can retrieve data ofinterest, such as client information (e.g., product information, clientrules, etc.), technical product details, news, current or predictedweather, and so on.

In addition to computing environment 214 collecting data (e.g., asreceived from network devices, such as sensors, and client devices orother sources) to be processed as part of a big data analytics project,it may also receive data in real time as part of a streaming analyticsenvironment. As noted, data may be collected using a variety of sourcesas communicated via different kinds of networks or locally. Such datamay be received on a real-time streaming basis. For example, networkdevices may receive data periodically from network device sensors as thesensors continuously sense, monitor and track changes in theirenvironments. Devices within computing environment 214 may also performpre-analysis on data it receives to determine if the data receivedshould be processed as part of an ongoing project. The data received andcollected by computing environment 214, no matter what the source ormethod or timing of receipt, may be processed over a period of time fora client to determine results data based on the client's needs andrules.

FIG. 3 illustrates a representation of a conceptual model of acommunications protocol system, according to embodiments of the presenttechnology. More specifically, FIG. 3 identifies operation of acomputing environment in an Open Systems Interaction model thatcorresponds to various connection components. The model 300 shows, forexample, how a computing environment, such as computing environment 314(or computing environment 214 in FIG. 2) may communicate with otherdevices in its network, and control how communications between thecomputing environment and other devices are executed and under whatconditions.

The model can include layers 301-307. The layers are arranged in astack. Each layer in the stack serves the layer one level higher than it(except for the application layer, which is the highest layer), and isserved by the layer one level below it (except for the physical layer,which is the lowest layer). The physical layer is the lowest layerbecause it receives and transmits raw bites of data, and is the farthestlayer from the user in a communications system. On the other hand, theapplication layer is the highest layer because it interacts directlywith a software application.

As noted, the model includes a physical layer 301. Physical layer 301represents physical communication, and can define parameters of thatphysical communication. For example, such physical communication maycome in the form of electrical, optical, or electromagnetic signals.Physical layer 301 also defines protocols that may controlcommunications within a data transmission network.

Link layer 302 defines links and mechanisms used to transmit (i.e.,move) data across a network. The link layer 302 manages node-to-nodecommunications, such as within a grid computing environment. Link layer302 can detect and correct errors (e.g., transmission errors in thephysical layer 301). Link layer 302 can also include a media accesscontrol (MAC) layer and logical link control (LLC) layer.

Network layer 303 defines the protocol for routing within a network. Inother words, the network layer coordinates transferring data acrossnodes in a same network (e.g., such as a grid computing environment).Network layer 303 can also define the processes used to structure localaddressing within the network.

Transport layer 304 can manage the transmission of data and the qualityof the transmission and/or receipt of that data. Transport layer 304 canprovide a protocol for transferring data, such as, for example, aTransmission Control Protocol (TCP). Transport layer 304 can assembleand disassemble data frames for transmission. The transport layer canalso detect transmission errors occurring in the layers below it.

Session layer 305 can establish, maintain, and manage communicationconnections between devices on a network. In other words, the sessionlayer controls the dialogues or nature of communications between networkdevices on the network. The session layer may also establishcheckpointing, adjournment, termination, and restart procedures.

Presentation layer 306 can provide translation for communicationsbetween the application and network layers. In other words, this layermay encrypt, decrypt and/or format data based on data types and/orencodings known to be accepted by an application or network layer.

Application layer 307 interacts directly with software applications andend users, and manages communications between them. Application layer307 can identify destinations, local resource states or availabilityand/or communication content or formatting using the applications.

Intra-network connection components 321 and 322 are shown to operate inlower levels, such as physical layer 301 and link layer 302,respectively. For example, a hub can operate in the physical layer, aswitch can operate in the link layer, and a router can operate in thenetwork layer. Inter-network connection components 323 and 328 are shownto operate on higher levels, such as layers 303-307. For example,routers can operate in the network layer and network devices can operatein the transport, session, presentation, and application layers.

As noted, a computing environment 314 can interact with and/or operateon, in various embodiments, one, more, all or any of the various layers.For example, computing environment 314 can interact with a hub (e.g.,via the link layer) so as to adjust which devices the hub communicateswith. The physical layer may be served by the link layer, so it mayimplement such data from the link layer. For example, the computingenvironment 314 may control which devices it will receive data from. Forexample, if the computing environment 314 knows that a certain networkdevice has turned off, broken, or otherwise become unavailable orunreliable, the computing environment 314 may instruct the hub toprevent any data from being transmitted to the computing environment 314from that network device. Such a process may be beneficial to avoidreceiving data that is inaccurate or that has been influenced by anuncontrolled environment. As another example, computing environment 314can communicate with a bridge, switch, router or gateway and influencewhich device within the system (e.g., system 200) the component selectsas a destination. In some embodiments, computing environment 314 caninteract with various layers by exchanging communications with equipmentoperating on a particular layer by routing or modifying existingcommunications. In another embodiment, such as in a grid computingenvironment, a node may determine how data within the environment shouldbe routed (e.g., which node should receive certain data) based oncertain parameters or information provided by other layers within themodel.

As noted, the computing environment 314 may be a part of acommunications grid environment, the communications of which may beimplemented as shown in the protocol of FIG. 3. For example, referringback to FIG. 2, one or more of machines 220 and 240 may be part of acommunications grid computing environment. A gridded computingenvironment may be employed in a distributed system with non-interactiveworkloads where data resides in memory on the machines, or computenodes. In such an environment, analytic code, instead of a databasemanagement system, controls the processing performed by the nodes. Datais co-located by pre-distributing it to the grid nodes, and the analyticcode on each node loads the local data into memory. Each node may beassigned a particular task such as a portion of a processing project, orto organize or control other nodes within the grid.

FIG. 4 illustrates a communications grid computing system 400 includinga variety of control and worker nodes, according to embodiments of thepresent technology. Communications grid computing system 400 includesthree control nodes and one or more worker nodes. Communications gridcomputing system 400 includes control nodes 402, 404, and 406. Thecontrol nodes are communicatively connected via communication paths 451,453, and 455. Therefore, the control nodes may transmit information(e.g., related to the communications grid or notifications), to andreceive information from each other. Although communications gridcomputing system 400 is shown in FIG. 4 as including three controlnodes, the communications grid may include more or less than threecontrol nodes.

Communications grid computing system (or just “communications grid”) 400also includes one or more worker nodes. Shown in FIG. 4 are six workernodes 410-420. Although FIG. 4 shows six worker nodes, a communicationsgrid according to embodiments of the present technology may include moreor less than six worker nodes. The number of worker nodes included in acommunications grid may be dependent upon how large the project or dataset is being processed by the communications grid, the capacity of eachworker node, the time designated for the communications grid to completethe project, among others. Each worker node within the communicationsgrid 400 may be connected (wired or wirelessly, and directly orindirectly) to control nodes 402-406. Therefore, each worker node mayreceive information from the control nodes (e.g., an instruction toperform work on a project) and may transmit information to the controlnodes (e.g., a result from work performed on a project). Furthermore,worker nodes may communicate with each other (either directly orindirectly). For example, worker nodes may transmit data between eachother related to a job being performed or an individual task within ajob being performed by that worker node. However, in certainembodiments, worker nodes may not, for example, be connected(communicatively or otherwise) to certain other worker nodes. In anembodiment, worker nodes may only be able to communicate with thecontrol node that controls it, and may not be able to communicate withother worker nodes in the communications grid, whether they are otherworker nodes controlled by the control node that controls the workernode, or worker nodes that are controlled by other control nodes in thecommunications grid.

A control node may connect with an external device with which thecontrol node may communicate (e.g., a grid user, such as a server orcomputer, may connect to a controller of the grid). For example, aserver or computer may connect to control nodes and may transmit aproject or job to the node. The project may include a data set. The dataset may be of any size. Once the control node receives such a projectincluding a large data set, the control node may distribute the data setor projects related to the data set to be performed by worker nodes.Alternatively, for a project including a large data set, the data setmay be received or stored by a machine other than a control node (e.g.,a HADOOP® standard-compliant data node employing the HADOOP® DistributedFile System, or HDFS).

Control nodes may maintain knowledge of the status of the nodes in thegrid (i.e., grid status information), accept work requests from clients,subdivide the work across worker nodes, coordinate the worker nodes,among other responsibilities. Worker nodes may accept work requests froma control node and provide the control node with results of the workperformed by the worker node. A grid may be started from a single node(e.g., a machine, computer, server, etc.). This first node may beassigned or may start as the primary control node that will control anyadditional nodes that enter the grid.

When a project is submitted for execution (e.g., by a client or acontroller of the grid) it may be assigned to a set of nodes. After thenodes are assigned to a project, a data structure (i.e., a communicator)may be created. The communicator may be used by the project forinformation to be shared between the project code running on each node.A communication handle may be created on each node. A handle, forexample, is a reference to the communicator that is valid within asingle process on a single node, and the handle may be used whenrequesting communications between nodes.

A control node, such as control node 402, may be designated as theprimary control node. A server, computer or other external device mayconnect to the primary control node. Once the control node receives aproject, the primary control node may distribute portions of the projectto its worker nodes for execution. For example, when a project isinitiated on communications grid 400, primary control node 402 controlsthe work to be performed for the project in order to complete theproject as requested or instructed. The primary control node maydistribute work to the worker nodes based on various factors, such aswhich subsets or portions of projects may be completed most efficientlyand in the correct amount of time. For example, a worker node mayperform analysis on a portion of data that is already local (e.g.,stored on) the worker node. The primary control node also coordinatesand processes the results of the work performed by each worker nodeafter each worker node executes and completes its job. For example, theprimary control node may receive a result from one or more worker nodes,and the control node may organize (e.g., collect and assemble) theresults received and compile them to produce a complete result for theproject received from the end user.

Any remaining control nodes, such as control nodes 404 and 406, may beassigned as backup control nodes for the project. In an embodiment,backup control nodes may not control any portion of the project.Instead, backup control nodes may serve as a backup for the primarycontrol node and take over as primary control node if the primarycontrol node were to fail. If a communications grid were to include onlya single control node, and the control node were to fail (e.g., thecontrol node is shut off or breaks) then the communications grid as awhole may fail and any project or job being run on the communicationsgrid may fail and may not complete. While the project may be run again,such a failure may cause a delay (severe delay in some cases, such asovernight delay) in completion of the project. Therefore, a grid withmultiple control nodes, including a backup control node, may bebeneficial.

To add another node or machine to the grid, the primary control node mayopen a pair of listening sockets, for example. A socket may be used toaccept work requests from clients, and the second socket may be used toaccept connections from other grid nodes. The primary control node maybe provided with a list of other nodes (e.g., other machines, computers,servers) that will participate in the grid, and the role that each nodewill fill in the grid. Upon startup of the primary control node (e.g.,the first node on the grid), the primary control node may use a networkprotocol to start the server process on every other node in the grid.Command line parameters, for example, may inform each node of one ormore pieces of information, such as: the role that the node will have inthe grid, the host name of the primary control node, the port number onwhich the primary control node is accepting connections from peer nodes,among others. The information may also be provided in a configurationfile, transmitted over a secure shell tunnel, recovered from aconfiguration server, among others. While the other machines in the gridmay not initially know about the configuration of the grid, thatinformation may also be sent to each other node by the primary controlnode. Updates of the grid information may also be subsequently sent tothose nodes.

For any control node other than the primary control node added to thegrid, the control node may open three sockets. The first socket mayaccept work requests from clients, the second socket may acceptconnections from other grid members, and the third socket may connect(e.g., permanently) to the primary control node. When a control node(e.g., primary control node) receives a connection from another controlnode, it first checks to see if the peer node is in the list ofconfigured nodes in the grid. If it is not on the list, the control nodemay clear the connection. If it is on the list, it may then attempt toauthenticate the connection. If authentication is successful, theauthenticating node may transmit information to its peer, such as theport number on which a node is listening for connections, the host nameof the node, information about how to authenticate the node, among otherinformation. When a node, such as the new control node, receivesinformation about another active node, it will check to see if italready has a connection to that other node. If it does not have aconnection to that node, it may then establish a connection to thatcontrol node.

Any worker node added to the grid may establish a connection to theprimary control node and any other control nodes on the grid. Afterestablishing the connection, it may authenticate itself to the grid(e.g., any control nodes, including both primary and backup, or a serveror user controlling the grid). After successful authentication, theworker node may accept configuration information from the control node.

When a node joins a communications grid (e.g., when the node is poweredon or connected to an existing node on the grid or both), the node isassigned (e.g., by an operating system of the grid) a universally uniqueidentifier (UUID). This unique identifier may help other nodes andexternal entities (devices, users, etc.) to identify the node anddistinguish it from other nodes. When a node is connected to the grid,the node may share its unique identifier with the other nodes in thegrid. Since each node may share its unique identifier, each node mayknow the unique identifier of every other node on the grid. Uniqueidentifiers may also designate a hierarchy of each of the nodes (e.g.,backup control nodes) within the grid. For example, the uniqueidentifiers of each of the backup control nodes may be stored in a listof backup control nodes to indicate an order in which the backup controlnodes will take over for a failed primary control node to become a newprimary control node. However, a hierarchy of nodes may also bedetermined using methods other than using the unique identifiers of thenodes. For example, the hierarchy may be predetermined, or may beassigned based on other predetermined factors.

The grid may add new machines at any time (e.g., initiated from anycontrol node). Upon adding a new node to the grid, the control node mayfirst add the new node to its table of grid nodes. The control node mayalso then notify every other control node about the new node. The nodesreceiving the notification may acknowledge that they have updated theirconfiguration information.

Primary control node 402 may, for example, transmit one or morecommunications to backup control nodes 404 and 406 (and, for example, toother control or worker nodes within the communications grid). Suchcommunications may sent periodically, at fixed time intervals, betweenknown fixed stages of the project's execution, among other protocols.The communications transmitted by primary control node 402 may be ofvaried types and may include a variety of types of information. Forexample, primary control node 402 may transmit snapshots (e.g., statusinformation) of the communications grid so that backup control node 404always has a recent snapshot of the communications grid. The snapshot orgrid status may include, for example, the structure of the grid(including, for example, the worker nodes in the grid, uniqueidentifiers of the nodes, or their relationships with the primarycontrol node) and the status of a project (including, for example, thestatus of each worker node's portion of the project). The snapshot mayalso include analysis or results received from worker nodes in thecommunications grid. The backup control nodes may receive and store thebackup data received from the primary control node. The backup controlnodes may transmit a request for such a snapshot (or other information)from the primary control node, or the primary control node may send suchinformation periodically to the backup control nodes.

As noted, the backup data may allow the backup control node to take overas primary control node if the primary control node fails withoutrequiring the grid to start the project over from scratch. If theprimary control node fails, the backup control node that will take overas primary control node may retrieve the most recent version of thesnapshot received from the primary control node and use the snapshot tocontinue the project from the stage of the project indicated by thebackup data. This may prevent failure of the project as a whole.

A backup control node may use various methods to determine that theprimary control node has failed. In one example of such a method, theprimary control node may transmit (e.g., periodically) a communicationto the backup control node that indicates that the primary control nodeis working and has not failed, such as a heartbeat communication. Thebackup control node may determine that the primary control node hasfailed if the backup control node has not received a heartbeatcommunication for a certain predetermined period of time. Alternatively,a backup control node may also receive a communication from the primarycontrol node itself (before it failed) or from a worker node that theprimary control node has failed, for example because the primary controlnode has failed to communicate with the worker node.

Different methods may be performed to determine which backup controlnode of a set of backup control nodes (e.g., backup control nodes 404and 406) will take over for failed primary control node 402 and becomethe new primary control node. For example, the new primary control nodemay be chosen based on a ranking or “hierarchy” of backup control nodesbased on their unique identifiers. In an alternative embodiment, abackup control node may be assigned to be the new primary control nodeby another device in the communications grid or from an external device(e.g., a system infrastructure or an end user, such as a server orcomputer, controlling the communications grid). In another alternativeembodiment, the backup control node that takes over as the new primarycontrol node may be designated based on bandwidth or other statisticsabout the communications grid.

A worker node within the communications grid may also fail. If a workernode fails, work being performed by the failed worker node may beredistributed amongst the operational worker nodes. In an alternativeembodiment, the primary control node may transmit a communication toeach of the operable worker nodes still on the communications grid thateach of the worker nodes should purposefully fail also. After each ofthe worker nodes fail, they may each retrieve their most recent savedcheckpoint of their status and re-start the project from that checkpointto minimize lost progress on the project being executed.

FIG. 5 illustrates a flow chart showing an example process 500 foradjusting a communications grid or a work project in a communicationsgrid after a failure of a node, according to embodiments of the presenttechnology. The process may include, for example, receiving grid statusinformation including a project status of a portion of a project beingexecuted by a node in the communications grid, as described in operation502. For example, a control node (e.g., a backup control node connectedto a primary control node and a worker node on a communications grid)may receive grid status information, where the grid status informationincludes a project status of the primary control node or a projectstatus of the worker node. The project status of the primary controlnode and the project status of the worker node may include a status ofone or more portions of a project being executed by the primary andworker nodes in the communications grid. The process may also includestoring the grid status information, as described in operation 504. Forexample, a control node (e.g., a backup control node) may store thereceived grid status information locally within the control node.Alternatively, the grid status information may be sent to another devicefor storage where the control node may have access to the information.

The process may also include receiving a failure communicationcorresponding to a node in the communications grid in operation 506. Forexample, a node may receive a failure communication including anindication that the primary control node has failed, prompting a backupcontrol node to take over for the primary control node. In analternative embodiment, a node may receive a failure that a worker nodehas failed, prompting a control node to reassign the work beingperformed by the worker node. The process may also include reassigning anode or a portion of the project being executed by the failed node, asdescribed in operation 508. For example, a control node may designatethe backup control node as a new primary control node based on thefailure communication upon receiving the failure communication. If thefailed node is a worker node, a control node may identify a projectstatus of the failed worker node using the snapshot of thecommunications grid, where the project status of the failed worker nodeincludes a status of a portion of the project being executed by thefailed worker node at the failure time.

The process may also include receiving updated grid status informationbased on the reassignment, as described in operation 510, andtransmitting a set of instructions based on the updated grid statusinformation to one or more nodes in the communications grid, asdescribed in operation 512. The updated grid status information mayinclude an updated project status of the primary control node or anupdated project status of the worker node. The updated information maybe transmitted to the other nodes in the grid to update their stalestored information.

FIG. 6 illustrates a portion of a communications grid computing system600 including a control node and a worker node, according to embodimentsof the present technology. Communications grid 600 computing systemincludes one control node (control node 602) and one worker node (workernode 610) for purposes of illustration, but may include more workerand/or control nodes. The control node 602 is communicatively connectedto worker node 610 via communication path 650. Therefore, control node602 may transmit information (e.g., related to the communications gridor notifications), to and receive information from worker node 610 viapath 650.

Similar to in FIG. 4, communications grid computing system (or just“communications grid”) 600 includes data processing nodes (control node602 and worker node 610). Nodes 602 and 610 include multi-core dataprocessors. Each node 602 and 610 includes a grid-enabled softwarecomponent (GESC) 620 that executes on the data processor associated withthat node and interfaces with buffer memory 622 also associated withthat node. Each node 602 and 610 includes a database management software(DBMS) 628 that executes on a database server (not shown) at controlnode 602 and on a database server (not shown) at worker node 610.

Each node also includes a data store 624. Data stores 624, similar tonetwork-attached data stores 110 in FIG. 1 and data stores 235 in FIG.2, are used to store data to be processed by the nodes in the computingenvironment. Data stores 624 may also store any intermediate or finaldata generated by the computing system after being processed, forexample in non-volatile memory. However in certain embodiments, theconfiguration of the grid computing environment allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory. Storing such data in volatile memory may be useful in certainsituations, such as when the grid receives queries (e.g., ad hoc) from aclient and when responses, which are generated by processing largeamounts of data, need to be generated quickly or on-the-fly. In such asituation, the grid may be configured to retain the data within memoryso that responses can be generated at different levels of detail and sothat a client may interactively query against this information.

Each node also includes a user-defined function (UDF) 626. The UDFprovides a mechanism for the DBMS 628 to transfer data to or receivedata from the database stored in the data stores 624 that are managed bythe DBMS. For example, UDF 626 can be invoked by the DBMS to providedata to the GESC for processing. The UDF 626 may establish a socketconnection (not shown) with the GESC to transfer the data.Alternatively, the UDF 626 can transfer data to the GESC by writing datato shared memory accessible by both the UDF and the GESC.

The GESC 620 at the nodes 602 and 620 may be connected via a network,such as network 108 shown in FIG. 1. Therefore, nodes 602 and 620 cancommunicate with each other via the network using a predeterminedcommunication protocol such as, for example, the Message PassingInterface (MPI). Each GESC 620 can engage in point-to-pointcommunication with the GESC at another node or in collectivecommunication with multiple GESCs via the network. The GESC 620 at eachnode may contain identical (or nearly identical) software instructions.Each node may be capable of operating as either a control node or aworker node. The GESC at the control node 602 can communicate, over acommunication path 652, with a client device 630. More specifically,control node 602 may communicate with client application 632 hosted bythe client device 630 to receive queries and to respond to those queriesafter processing large amounts of data.

DBMS 628 may control the creation, maintenance, and use of database ordata structure (not shown) within a nodes 602 or 610. The database mayorganize data stored in data stores 624. The DBMS 628 at control node602 may accept requests for data and transfer the appropriate data forthe request. With such a process, collections of data may be distributedacross multiple physical locations. In this example, each node 602 and610 stores a portion of the total data managed by the management systemin its associated data store 624.

Furthermore, the DBMS may be responsible for protecting against dataloss using replication techniques. Replication includes providing abackup copy of data stored on one node on one or more other nodes.Therefore, if one node fails, the data from the failed node can berecovered from a replicated copy residing at another node. However, asdescribed herein with respect to FIG. 4, data or status information foreach node in the communications grid may also be shared with each nodeon the grid.

FIG. 7 illustrates a flow chart showing an example method 700 forexecuting a project within a grid computing system, according toembodiments of the present technology. As described with respect to FIG.6, the GESC at the control node may transmit data with a client device(e.g., client device 630) to receive queries for executing a project andto respond to those queries after large amounts of data have beenprocessed. The query may be transmitted to the control node, where thequery may include a request for executing a project, as described inoperation 702. The query can contain instructions on the type of dataanalysis to be performed in the project and whether the project shouldbe executed using the grid-based computing environment, as shown inoperation 704.

To initiate the project, the control node may determine if the queryrequests use of the grid-based computing environment to execute theproject. If the determination is no, then the control node initiatesexecution of the project in a solo environment (e.g., at the controlnode), as described in operation 710. If the determination is yes, thecontrol node may initiate execution of the project in the grid-basedcomputing environment, as described in operation 706. In such asituation, the request may include a requested configuration of thegrid. For example, the request may include a number of control nodes anda number of worker nodes to be used in the grid when executing theproject. After the project has been completed, the control node maytransmit results of the analysis yielded by the grid, as described inoperation 708. Whether the project is executed in a solo or grid-basedenvironment, the control node provides the results of the project, asdescribed in operation 712.

As noted with respect to FIG. 2, the computing environments describedherein may collect data (e.g., as received from network devices, such assensors, such as network devices 204-209 in FIG. 2, and client devicesor other sources) to be processed as part of a data analytics project,and data may be received in real time as part of a streaming analyticsenvironment (e.g., ESP). Data may be collected using a variety ofsources as communicated via different kinds of networks or locally, suchas on a real-time streaming basis. For example, network devices mayreceive data periodically from network device sensors as the sensorscontinuously sense, monitor and track changes in their environments.More specifically, an increasing number of distributed applicationsdevelop or produce continuously flowing data from distributed sources byapplying queries to the data before distributing the data togeographically distributed recipients. An event stream processing engine(ESPE) may continuously apply the queries to the data as it is receivedand determines which entities should receive the data. Client or otherdevices may also subscribe to the ESPE or other devices processing ESPdata so that they can receive data after processing, based on forexample the entities determined by the processing engine. For example,client devices 230 in FIG. 2 may subscribe to the ESPE in computingenvironment 214. In another example, event subscription devices 1024a-c, described further with respect to FIG. 10, may also subscribe tothe ESPE. The ESPE may determine or define how input data or eventstreams from network devices or other publishers (e.g., network devices204-209 in FIG. 2) are transformed into meaningful output data to beconsumed by subscribers, such as for example client devices 230 in FIG.2.

FIG. 8 illustrates a block diagram including components of an EventStream Processing Engine (ESPE), according to embodiments of the presenttechnology. ESPE 800 may include one or more projects 802. A project maybe described as a second-level container in an engine model managed byESPE 800 where a thread pool size for the project may be defined by auser. Each project of the one or more projects 802 may include one ormore continuous queries 804 that contain data flows, which are datatransformations of incoming event streams. The one or more continuousqueries 804 may include one or more source windows 806 and one or morederived windows 808.

The ESPE may receive streaming data over a period of time related tocertain events, such as events or other data sensed by one or morenetwork devices. The ESPE may perform operations associated withprocessing data created by the one or more devices. For example, theESPE may receive data from the one or more network devices 204-209 shownin FIG. 2. As noted, the network devices may include sensors that sensedifferent aspects of their environments, and may collect data over timebased on those sensed observations. For example, the ESPE may beimplemented within one or more of machines 220 and 240 shown in FIG. 2.The ESPE may be implemented within such a machine by an ESP application.An ESP application may embed an ESPE with its own dedicated thread poolor pools into its application space where the main application threadcan do application-specific work and the ESPE processes event streams atleast by creating an instance of a model into processing objects.

The engine container is the top-level container in a model that managesthe resources of the one or more projects 802. In an illustrativeembodiment, for example, there may be only one ESPE 800 for eachinstance of the ESP application, and ESPE 800 may have a unique enginename. Additionally, the one or more projects 802 may each have uniqueproject names, and each query may have a unique continuous query nameand begin with a uniquely named source window of the one or more sourcewindows 806. ESPE 800 may or may not be persistent.

Continuous query modeling involves defining directed graphs of windowsfor event stream manipulation and transformation. A window in thecontext of event stream manipulation and transformation is a processingnode in an event stream processing model. A window in a continuous querycan perform aggregations, computations, pattern-matching, and otheroperations on data flowing through the window. A continuous query may bedescribed as a directed graph of source, relational, pattern matching,and procedural windows. The one or more source windows 806 and the oneor more derived windows 808 represent continuously executing queriesthat generate updates to a query result set as new event blocks streamthrough ESPE 800. A directed graph, for example, is a set of nodesconnected by edges, where the edges have a direction associated withthem.

An event object may be described as a packet of data accessible as acollection of fields, with at least one of the fields defined as a keyor unique identifier (ID). The event object may be created using avariety of formats including binary, alphanumeric, XML, etc. Each eventobject may include one or more fields designated as a primary identifier(ID) for the event so ESPE 800 can support operation codes (opcodes) forevents including insert, update, upsert, and delete. Upsert opcodesupdate the event if the key field already exists; otherwise, the eventis inserted. For illustration, an event object may be a packed binaryrepresentation of a set of field values and include both metadata andfield data associated with an event. The metadata may include an opcodeindicating if the event represents an insert, update, delete, or upsert,a set of flags indicating if the event is a normal, partial-update, or aretention generated event from retention policy management, and a set ofmicrosecond timestamps that can be used for latency measurements.

An event block object may be described as a grouping or package of eventobjects. An event stream may be described as a flow of event blockobjects. A continuous query of the one or more continuous queries 804transforms a source event stream made up of streaming event blockobjects published into ESPE 800 into one or more output event streamsusing the one or more source windows 806 and the one or more derivedwindows 808. A continuous query can also be thought of as data flowmodeling.

The one or more source windows 806 are at the top of the directed graphand have no windows feeding into them. Event streams are published intothe one or more source windows 806, and from there, the event streamsmay be directed to the next set of connected windows as defined by thedirected graph. The one or more derived windows 808 are all instantiatedwindows that are not source windows and that have other windowsstreaming events into them. The one or more derived windows 808 mayperform computations or transformations on the incoming event streams.The one or more derived windows 808 transform event streams based on thewindow type (that is operators such as join, filter, compute, aggregate,copy, pattern match, procedural, union, etc.) and window settings. Asevent streams are published into ESPE 800, they are continuouslyqueried, and the resulting sets of derived windows in these queries arecontinuously updated.

FIG. 9 illustrates a flow chart showing an example process includingoperations performed by an event stream processing engine, according tosome embodiments of the present technology. As noted, the ESPE 800 (oran associated ESP application) defines how input event streams aretransformed into meaningful output event streams. More specifically, theESP application may define how input event streams from publishers(e.g., network devices providing sensed data) are transformed intomeaningful output event streams consumed by subscribers (e.g., a dataanalytics project being executed by a machine or set of machines).

Within the application, a user may interact with one or more userinterface windows presented to the user in a display under control ofthe ESPE independently or through a browser application in an orderselectable by the user. For example, a user may execute an ESPapplication, which causes presentation of a first user interface window,which may include a plurality of menus and selectors such as drop downmenus, buttons, text boxes, hyperlinks, etc. associated with the ESPapplication as understood by a person of skill in the art. As furtherunderstood by a person of skill in the art, various operations may beperformed in parallel, for example, using a plurality of threads.

At operation 900, an ESP application may define and start an ESPE,thereby instantiating an ESPE at a device, such as machine 220 and/or240. In an operation 902, the engine container is created. Forillustration, ESPE 800 may be instantiated using a function call thatspecifies the engine container as a manager for the model.

In an operation 904, the one or more continuous queries 804 areinstantiated by ESPE 800 as a model. The one or more continuous queries804 may be instantiated with a dedicated thread pool or pools thatgenerate updates as new events stream through ESPE 800. Forillustration, the one or more continuous queries 804 may be created tomodel business processing logic within ESPE 800, to predict eventswithin ESPE 800, to model a physical system within ESPE 800, to predictthe physical system state within ESPE 800, etc. For example, as noted,ESPE 800 may be used to support sensor data monitoring and management(e.g., sensing may include force, torque, load, strain, position,temperature, air pressure, fluid flow, chemical properties, resistance,electromagnetic fields, radiation, irradiance, proximity, acoustics,moisture, distance, speed, vibrations, acceleration, electricalpotential, or electrical current, etc.).

ESPE 800 may analyze and process events in motion or “event streams.”Instead of storing data and running queries against the stored data,ESPE 800 may store queries and stream data through them to allowcontinuous analysis of data as it is received. The one or more sourcewindows 806 and the one or more derived windows 808 may be created basedon the relational, pattern matching, and procedural algorithms thattransform the input event streams into the output event streams tomodel, simulate, score, test, predict, etc. based on the continuousquery model defined and application to the streamed data.

In an operation 906, a publish/subscribe (pub/sub) capability isinitialized for ESPE 800. In an illustrative embodiment, a pub/subcapability is initialized for each project of the one or more projects802. To initialize and enable pub/sub capability for ESPE 800, a portnumber may be provided. Pub/sub clients can use a host name of an ESPdevice running the ESPE and the port number to establish pub/subconnections to ESPE 800.

FIG. 10 illustrates an ESP system 1000 interfacing between publishingdevice 1022 and event subscribing devices 1024 a-c, according toembodiments of the present technology. ESP system 1000 may include ESPdevice or subsystem 851, event publishing device 1022, an eventsubscribing device A 1024 a, an event subscribing device B 1024 b, andan event subscribing device C 1024 c. Input event streams are output toESP device 851 by publishing device 1022. In alternative embodiments,the input event streams may be created by a plurality of publishingdevices. The plurality of publishing devices further may publish eventstreams to other ESP devices. The one or more continuous queriesinstantiated by ESPE 800 may analyze and process the input event streamsto form output event streams output to event subscribing device A 1024a, event subscribing device B 1024 b, and event subscribing device C1024 c. ESP system 1000 may include a greater or a fewer number of eventsubscribing devices of event subscribing devices.

Publish-subscribe is a message-oriented interaction paradigm based onindirect addressing. Processed data recipients specify their interest inreceiving information from ESPE 800 by subscribing to specific classesof events, while information sources publish events to ESPE 800 withoutdirectly addressing the receiving parties. ESPE 800 coordinates theinteractions and processes the data. In some cases, the data sourcereceives confirmation that the published information has been receivedby a data recipient.

A publish/subscribe API may be described as a library that enables anevent publisher, such as publishing device 1022, to publish eventstreams into ESPE 800 or an event subscriber, such as event subscribingdevice A 1024 a, event subscribing device B 1024 b, and eventsubscribing device C 1024 c, to subscribe to event streams from ESPE800. For illustration, one or more publish/subscribe APIs may bedefined. Using the publish/subscribe API, an event publishingapplication may publish event streams into a running event streamprocessor project source window of ESPE 800, and the event subscriptionapplication may subscribe to an event stream processor project sourcewindow of ESPE 800.

The publish/subscribe API provides cross-platform connectivity andendianness compatibility between ESP application and other networkedapplications, such as event publishing applications instantiated atpublishing device 1022, and event subscription applications instantiatedat one or more of event subscribing device A 1024 a, event subscribingdevice B 1024 b, and event subscribing device C 1024 c.

Referring back to FIG. 9, operation 906 initializes thepublish/subscribe capability of ESPE 800. In an operation 908, the oneor more projects 802 are started. The one or more started projects mayrun in the background on an ESP device. In an operation 910, an eventblock object is received from one or more computing device of the eventpublishing device 1022.

ESP subsystem 800 may include a publishing client 1002, ESPE 800, asubscribing client A 1004, a subscribing client B 1006, and asubscribing client C 1008. Publishing client 1002 may be started by anevent publishing application executing at publishing device 1022 usingthe publish/subscribe API. Subscribing client A 1004 may be started byan event subscription application A, executing at event subscribingdevice A 1024 a using the publish/subscribe API. Subscribing client B1006 may be started by an event subscription application B executing atevent subscribing device B 1024 b using the publish/subscribe API.Subscribing client C 1008 may be started by an event subscriptionapplication C executing at event subscribing device C 1024 c using thepublish/subscribe API.

An event block object containing one or more event objects is injectedinto a source window of the one or more source windows 806 from aninstance of an event publishing application on event publishing device1022. The event block object may generated, for example, by the eventpublishing application and may be received by publishing client 1002. Aunique ID may be maintained as the event block object is passed betweenthe one or more source windows 806 and/or the one or more derivedwindows 808 of ESPE 800, and to subscribing client A 1004, subscribingclient B 1006, and subscribing client C 1008 and to event subscriptiondevice A 1024 a, event subscription device B 1024 b, and eventsubscription device C 1024 c. Publishing client 1002 may furthergenerate and include a unique embedded transaction ID in the event blockobject as the event block object is processed by a continuous query, aswell as the unique ID that publishing device 1022 assigned to the eventblock object.

In an operation 912, the event block object is processed through the oneor more continuous queries 804. In an operation 914, the processed eventblock object is output to one or more computing devices of the eventsubscribing devices 1024 a-c. For example, subscribing client A 1004,subscribing client B 1006, and subscribing client C 1008 may send thereceived event block object to event subscription device A 1024 a, eventsubscription device B 1024 b, and event subscription device C 1024 c,respectively.

ESPE 800 maintains the event block containership aspect of the receivedevent blocks from when the event block is published into a source windowand works its way through the directed graph defined by the one or morecontinuous queries 804 with the various event translations before beingoutput to subscribers. Subscribers can correlate a group of subscribedevents back to a group of published events by comparing the unique ID ofthe event block object that a publisher, such as publishing device 1022,attached to the event block object with the event block ID received bythe subscriber.

In an operation 916, a determination is made concerning whether or notprocessing is stopped. If processing is not stopped, processingcontinues in operation 910 to continue receiving the one or more eventstreams containing event block objects from the, for example, one ormore network devices. If processing is stopped, processing continues inan operation 918. In operation 918, the started projects are stopped. Inoperation 920, the ESPE is shutdown.

As noted, in some embodiments, big data is processed for an analyticsproject after the data is received and stored. In other embodiments,distributed applications process continuously flowing data in real-timefrom distributed sources by applying queries to the data beforedistributing the data to geographically distributed recipients. Asnoted, an event stream processing engine (ESPE) may continuously applythe queries to the data as it is received and determines which entitiesreceive the processed data. This allows for large amounts of data beingreceived and/or collected in a variety of environments to be processedand distributed in real time. For example, as shown with respect to FIG.2, data may be collected from network devices that may include deviceswithin the internet of things, such as devices within a home automationnetwork. However, such data may be collected from a variety of differentresources in a variety of different environments. In any such situation,embodiments of the present technology allow for real-time processing ofsuch data.

Aspects of the current disclosure provide technical solutions totechnical problems, such as computing problems that arise when an ESPdevice fails which results in a complete service interruption andpotentially significant data loss. The data loss can be catastrophicwhen the streamed data is supporting mission critical operations such asthose in support of an ongoing manufacturing or drilling operation. Anembodiment of an ESP system achieves a rapid and seamless failover ofESPE running at the plurality of ESP devices without serviceinterruption or data loss, thus significantly improving the reliabilityof an operational system that relies on the live or real-time processingof the data streams. The event publishing systems, the event subscribingsystems, and each ESPE not executing at a failed ESP device are notaware of or effected by the failed ESP device. The ESP system mayinclude thousands of event publishing systems and event subscribingsystems. The ESP system keeps the failover logic and awareness withinthe boundaries of out-messaging network connector and out-messagingnetwork device.

In one example embodiment, a system is provided to support a failoverwhen event stream processing (ESP) event blocks. The system includes,but is not limited to, an out-messaging network device and a computingdevice. The computing device includes, but is not limited to, aprocessor and a computer-readable medium operably coupled to theprocessor. The processor is configured to execute an ESP engine (ESPE).The computer-readable medium has instructions stored thereon that, whenexecuted by the processor, cause the computing device to support thefailover. An event block object is received from the ESPE that includesa unique identifier. A first status of the computing device as active orstandby is determined. When the first status is active, a second statusof the computing device as newly active or not newly active isdetermined. Newly active is determined when the computing device isswitched from a standby status to an active status. When the secondstatus is newly active, a last published event block object identifierthat uniquely identifies a last published event block object isdetermined. A next event block object is selected from a non-transitorycomputer-readable medium accessible by the computing device. The nextevent block object has an event block object identifier that is greaterthan the determined last published event block object identifier. Theselected next event block object is published to an out-messagingnetwork device. When the second status of the computing device is notnewly active, the received event block object is published to theout-messaging network device. When the first status of the computingdevice is standby, the received event block object is stored in thenon-transitory computer-readable medium.

FIG. 11 is a flow chart of an example of a process for generating andusing a machine-learning model according to some aspects. Machinelearning is a branch of artificial intelligence that relates tomathematical models that can learn from, categorize, and makepredictions about data. Such mathematical models, which can be referredto as machine-learning models, can classify input data among two or moreclasses; cluster input data among two or more groups; predict a resultbased on input data; identify patterns or trends in input data; identifya distribution of input data in a space; or any combination of these.Examples of machine-learning models can include (i) neural networks;(ii) decision trees, such as classification trees and regression trees;(iii) classifiers, such as Naïve bias classifiers, logistic regressionclassifiers, ridge regression classifiers, random forest classifiers,least absolute shrinkage and selector (LASSO) classifiers, and supportvector machines; (iv) clusterers, such as k-means clusterers, mean-shiftclusterers, and spectral clusterers; (v) factorizers, such asfactorization machines, principal component analyzers and kernelprincipal component analyzers; and (vi) ensembles or other combinationsof machine-learning models. In some examples, neural networks caninclude deep neural networks, feed-forward neural networks, recurrentneural networks, convolutional neural networks, radial basis function(RBF) neural networks, echo state neural networks, long short-termmemory neural networks, bi-directional recurrent neural networks, gatedneural networks, hierarchical recurrent neural networks, stochasticneural networks, modular neural networks, spiking neural networks,dynamic neural networks, cascading neural networks, neuro-fuzzy neuralnetworks, or any combination of these.

Different machine-learning models may be used interchangeably to performa task. Examples of tasks that can be performed at least partially usingmachine-learning models include various types of scoring;bioinformatics; cheminformatics; software engineering; fraud detection;customer segmentation; generating online recommendations; adaptivewebsites; determining customer lifetime value; search engines; placingadvertisements in real time or near real time; classifying DNAsequences; affective computing; performing natural language processingand understanding; object recognition and computer vision; roboticlocomotion; playing games; optimization and metaheuristics; detectingnetwork intrusions; medical diagnosis and monitoring; or predicting whenan asset, such as a machine, will need maintenance.

Any number and combination of tools can be used to createmachine-learning models. Examples of tools for creating and managingmachine-learning models can include SAS® Enterprise Miner, SAS® RapidPredictive Modeler, and SAS® Model Manager, SAS Cloud Analytic Services(CAS)®, SAS Viya® of all which are by SAS Institute Inc. of Cary, N.C.

Machine-learning models can be constructed through an at least partiallyautomated (e.g., with little or no human involvement) process calledtraining. During training, input data can be iteratively supplied to amachine-learning model to enable the machine-learning model to identifypatterns related to the input data or to identify relationships betweenthe input data and output data. With training, the machine-learningmodel can be transformed from an untrained state to a trained state.Input data can be split into one or more training sets and one or morevalidation sets, and the training process may be repeated multipletimes. The splitting may follow a k-fold cross-validation rule, aleave-one-out-rule, a leave-p-out rule, or a holdout rule. An overviewof training and using a machine-learning model is described below withrespect to the flow chart of FIG. 11.

In block 1104, training data is received. In some examples, the trainingdata is received from a remote database or a local database, constructedfrom various subsets of data, or input by a user. The training data canbe used in its raw form for training a machine-learning model orpre-processed into another form, which can then be used for training themachine-learning model. For example, the raw form of the training datacan be smoothed, truncated, aggregated, clustered, or otherwisemanipulated into another form, which can then be used for training themachine-learning model.

In block 1106, a machine-learning model is trained using the trainingdata. The machine-learning model can be trained in a supervised,unsupervised, or semi-supervised manner. In supervised training, eachinput in the training data is correlated to a desired output. Thisdesired output may be a scalar, a vector, or a different type of datastructure such as text or an image. This may enable the machine-learningmodel to learn a mapping between the inputs and desired outputs. Inunsupervised training, the training data includes inputs, but notdesired outputs, so that the machine-learning model has to findstructure in the inputs on its own. In semi-supervised training, onlysome of the inputs in the training data are correlated to desiredoutputs.

In block 1108, the machine-learning model is evaluated. For example, anevaluation dataset can be obtained, for example, via user input or froma database. The evaluation dataset can include inputs correlated todesired outputs. The inputs can be provided to the machine-learningmodel and the outputs from the machine-learning model can be compared tothe desired outputs. If the outputs from the machine-learning modelclosely correspond with the desired outputs, the machine-learning modelmay have a high degree of accuracy. For example, if 90% or more of theoutputs from the machine-learning model are the same as the desiredoutputs in the evaluation dataset, the machine-learning model may have ahigh degree of accuracy. Otherwise, the machine-learning model may havea low degree of accuracy. The 90% number is an example only. A realisticand desirable accuracy percentage is dependent on the problem and thedata.

In some examples, if the machine-learning model has an inadequate degreeof accuracy for a particular task, the process can return to block 1106,where the machine-learning model can be further trained using additionaltraining data or otherwise modified to improve accuracy. If themachine-learning model has an adequate degree of accuracy for theparticular task, the process can continue to block 1110.

In block 1110, new data is received. In some examples, the new data isreceived from a remote database or a local database, constructed fromvarious subsets of data, or input by a user. The new data may be unknownto the machine-learning model. For example, the machine-learning modelmay not have previously processed or analyzed the new data.

In block 1112, the trained machine-learning model is used to analyze thenew data and provide a result. For example, the new data can be providedas input to the trained machine-learning model. The trainedmachine-learning model can analyze the new data and provide a resultthat includes a classification of the new data into a particular class,a clustering of the new data into a particular group, a prediction basedon the new data, or any combination of these.

In block 1114, the result is post-processed. For example, the result canbe added to, multiplied with, or otherwise combined with other data aspart of a job. As another example, the result can be transformed from afirst format, such as a time series format, into another format, such asa count series format. Any number and combination of operations can beperformed on the result during post-processing.

A more specific example of a machine-learning model is the neuralnetwork 1200 shown in FIG. 12. The neural network 1200 is represented asmultiple layers of interconnected neurons, such as neuron 1208, that canexchange data between one another. The layers include an input layer1202 for receiving input data, a hidden layer 1204, and an output layer1206 for providing a result. The hidden layer 1204 is referred to ashidden because it may not be directly observable or have its inputdirectly accessible during the normal functioning of the neural network1200. Although the neural network 1200 is shown as having a specificnumber of layers and neurons for exemplary purposes, the neural network1200 can have any number and combination of layers, and each layer canhave any number and combination of neurons.

The neurons and connections between the neurons can have numericweights, which can be tuned during training. For example, training datacan be provided to the input layer 1202 of the neural network 1200, andthe neural network 1200 can use the training data to tune one or morenumeric weights of the neural network 1200. In some examples, the neuralnetwork 1200 can be trained using backpropagation. Backpropagation caninclude determining a gradient of a particular numeric weight based on adifference between an actual output of the neural network 1200 and adesired output of the neural network 1200. Based on the gradient, one ormore numeric weights of the neural network 1200 can be updated to reducethe difference, thereby increasing the accuracy of the neural network1200. This process can be repeated multiple times to train the neuralnetwork 1200. For example, this process can be repeated hundreds orthousands of times to train the neural network 1200.

In some examples, the neural network 1200 is a feed-forward neuralnetwork. In a feed-forward neural network, every neuron only propagatesan output value to a subsequent layer of the neural network 1200. Forexample, data may only move one direction (forward) from one neuron tothe next neuron in a feed-forward neural network.

In other examples, the neural network 1200 is a recurrent neuralnetwork. A recurrent neural network can include one or more feedbackloops, allowing data to propagate in both forward and backward throughthe neural network 1200. This can allow for information to persistwithin the recurrent neural network. For example, a recurrent neuralnetwork can determine an output based at least partially on informationthat the recurrent neural network has seen before, giving the recurrentneural network the ability to use previous input to inform the output.

In some examples, the neural network 1200 operates by receiving a vectorof numbers from one layer; transforming the vector of numbers into a newvector of numbers using a matrix of numeric weights, a nonlinearity, orboth; and providing the new vector of numbers to a subsequent layer ofthe neural network 1200. Each subsequent layer of the neural network1200 can repeat this process until the neural network 1200 outputs afinal result at the output layer 1206. For example, the neural network1200 can receive a vector of numbers as an input at the input layer1202. The neural network 1200 can multiply the vector of numbers by amatrix of numeric weights to determine a weighted vector. The matrix ofnumeric weights can be tuned during the training of the neural network1200. The neural network 1200 can transform the weighted vector using anonlinearity, such as a sigmoid tangent or the hyperbolic tangent. Insome examples, the nonlinearity can include a rectified linear unit,which can be expressed using the equation y=max(x, 0) where y is theoutput and x is an input value from the weighted vector. The transformedoutput can be supplied to a subsequent layer, such as the hidden layer1204, of the neural network 1200. The subsequent layer of the neuralnetwork 1200 can receive the transformed output, multiply thetransformed output by a matrix of numeric weights and a nonlinearity,and provide the result to yet another layer of the neural network 1200.This process continues until the neural network 1200 outputs a finalresult at the output layer 1206.

Other examples of the present disclosure may include any number andcombination of machine-learning models having any number and combinationof characteristics. The machine-learning model(s) can be trained in asupervised, semi-supervised, or unsupervised manner, or any combinationof these. The machine-learning model(s) can be implemented using asingle computing device or multiple computing devices, such as thecommunications grid computing system 400 discussed above.

Implementing some examples of the present disclosure at least in part byusing machine-learning models can reduce the total number of processingiterations, time, memory, electrical power, or any combination of theseconsumed by a computing device when analyzing data. For example, aneural network may more readily identify patterns in data than otherapproaches. This may enable the neural network to analyze the data usingfewer processing cycles and less memory than other approaches, whileobtaining a similar or greater level of accuracy.

FIG. 13A illustrates a block diagram of an example embodiment of adistributed processing system 2000 incorporating one or more datadevices 2100, one or more node devices 2300 that form of a node devicegrid 2003, at least one control device 2500 and/or at least onerequesting device 2700 coupled by a network 2999. FIG. 13B illustrates ablock diagram of an alternate example embodiment of the distributedprocessing system 2000 in which one of the node devices 2300 mayincorporate a controller 2503 to perform the functions of the controldevice 2500. In both of the embodiments of FIGS. 13A and 13B, the datadevice(s) 2100 may provide the node device(s) 2300 a data set 2230 (ordata values by which the node device(s) 2300 may assemble the data set2230), and the node device(s) 2300 may perform any of a variety ofoperations with the data set 2230 under the control of the controldevice 2500 (or controller 2503). Such operations may be performed inresponse to instances of query instructions 2710 transmitted to thecontrol device 2500 (or controller 2503) by one or more of therequesting device 2700, and results data 2770 indicative of the resultsof such operations may be transmitted by the control device 2500 (orcontroller 2503) back to the requesting device 2700. To enable theperformance of such operations by the node device(s) 2300, the datadevice(s) 2100 may provide the node device(s) 2300 with a data set index2530. Alternatively, the node device(s) 2300 may generate the data setindex 2530.

In support of such operations, the devices 2100, 2300, 2500 and/or 2700may exchange portions of the data set 2230, portions of the data setindex 2530, the query instructions 2710, the results data 2770, and/orother commands and/or data concerning assembling, indexing, searchingand/or performing operations with the data set 2230. In variousembodiments, the network 2999 may be a single network that may extendwithin a single building or other relatively limited area, a combinationof connected networks that may extend a considerable distance, and/ormay include the Internet. Thus, the network 2999 may be based on any ofa variety (or combination) of communications technologies by whichcommunications may be effected, including without limitation, wiredtechnologies employing electrically and/or optically conductive cabling,and wireless technologies employing infrared, radio frequency (RF) orother forms of wireless transmission.

The data of a data set 2230 may be any of a variety of types of dataconcerning any of a wide variety of subjects, including and not limitedto, technical or scientific data, patient or sociological data, shippingor activity tracking data, historical or real-time event data,geological or meteorological data, etc. As will be explained in greaterdetail, it is envisioned that the data set 2230 may be sufficientlylarge in size such that storage and/or processing of the entirety of thedata set 2230 within a single device may be deemed to be at leastimpractical, if not impossible. Therefore, to facilitate storage and/orprocessing of the data set 2230 in a distributed manner across multipledevices, the data set 2230 may be divided into multiple super cells2233, that may each be divided into multiple data cells 2130, that mayeach, in turn, be divided into multiple data records 2133. As will alsobe explained in greater detail, to enable distribution of the data setindex 2530 alongside the data set 2230, the data set index 2530 may bedivided up into multiple super cell indexes 2430 that each correspond toone of the super cells 2233, and cell indexes 2330 that each correspondto one of the data cells 2130.

In some embodiments, the data devices 2100 may form a grid or other typeof assemblage of multiple storage devices (e.g., a network-attacheddrive array, etc.) that may serve primarily to store data, such as thedata set 2230. In such embodiments, the data devices 2100 may be capableof exchanging the entirety of the data set 2230 with the node devices2300 in a set of data transfers through the network 2999 that may becoordinated by the control device 2500 (or controller 2503). In such anexchange of the data set 2230, the data set index 2530 may be exchangedalong with the data set 2230.

However, in other embodiments, the data devices 2100 may be a multitudeof devices of any of a variety of types that each provide data valuesand/or streams of data values to the node devices 2300 to enable thenode devices 2300 to assemble the data set 2230 therefrom. In such otherembodiments, each of the data devices 2100 may be any of a variety oftypes of device that may serve as a source of data. More specifically,and depending on the content of the data set 2230, each of the datadevices 2100 may be any of a variety of types of sensor taking physicalor other measurements, any of a variety of types of recording devicethat records audio/video and/or other physical phenomenon, any of avariety of types of server that provides an online service and/ormaintains accounts for the provision of services, any of a variety oftypes of equipment that provides a utility or building operationsservice, any of a variety of types of Internet-attached appliance thatperforms a function in a commercial or home environment, etc. In suchother embodiments, in addition to assembling the data set 2230 from datavalues and/or streams of data values received from the data devices2100, the node devices 2300 may recurringly generate the data set index2530 from the data set 2230 as the data set 2230 is so assembled and/orupdated.

In various embodiments, each of the multiple node devices 2300 mayincorporate one or more of a processor 2350, a storage 2360 and anetwork interface 2390 to couple each of the node devices 2300 to thenetwork 2999. The storage 2360 may store a portion of the data set 2230,a corresponding portion of the data set index 2530, a control routine2340 and/or parsing data 2310. As indicated with dotted lines, and aswill be explained in greater detail, the data set index 2530 may beincorporated into the data set 2230 in some embodiments. Alternatively,and as will also be explained in greater detail, the data set 2230 mayat least routinely be accompanied by the data set index 2530 wheneverand/or wherever the data set 2230 is exchanged between devices and/orstored. The control routine 2340 may incorporate a sequence ofinstructions operative on the processor 2350 to implement logic toperform various functions. In some embodiments, in executing the controlroutine 2340, the processor 2350 of each of the node devices 2300 may becaused to operate the network interface 2390 to receive a portion of thedata set 2230 and/or a corresponding portion of the data set index 2530from the data devices 2100 via the network 2999 at least partially inparallel with others of the node devices 2300. In other embodiments, inexecuting the control routine 2340, the processor 2350 of each of thenode devices 2300 may be caused to operate the network interface 2390 toreceive data values, or one or more streams of data values, from one ormore data devices 2100 via the network 2999, and may assemble a portionof the data set 2230 from the received data values at least partially inparallel with others of the node devices 2300. In such otherembodiments, the processor 2350 of each of the node devices 2300 may becaused to recurringly generate a corresponding portion of the data setindex 2530.

Regardless of whether the data set 2230 and/or the data set index 2530are received by or generated by the node devices 2300, in furtherexecuting the control routine 2340, the processor 2350 of each of atleast a subset of the node devices 2300 may receive instructions fromthe control device 2500 (or the controller 2503) to perform one or morespecified operations with the data set 2230, at least partially inparallel with others of the node devices 2300, and to transmit theresults of such operations back to the control device 2500 (or thecontroller 2503) via the network 2999. Among such received instructionsmay be a relayed copy of the query instructions 2710, which may includeinstructions to at least perform a search of the data set 2230 for onemore data records 2133 that meet search criteria specified in the queryinstructions 2710. In some embodiments, instances of the queryinstructions 2710 may additionally include task instructions for theperformance of one or more additional processing operations with datavalues retrieved by the search to perform a processing task therewith.

In various embodiments, the control device 2500 may incorporate one ormore of a processor 2550, a storage 2560 and a network interface 2590 tocouple the control device 2500 to the network 2999. The storage 2560 maystore rules data 2510, node data 2539, a control routine 2540, multipleones of the super cell indexes 2430, and/or the results data 2770. Thecontrol routine 2540 may incorporate a sequence of instructionsoperative on the processor 2550 to implement logic to perform variousfunctions. In executing the control routine 2540, the processor 2550 maybe caused to operate the network interface 2590 to coordinate theaforedescribed at least partially parallel exchanges of the data set2230 and/or the data set index 2530 between the data devices 2100 andthe node devices 2300 through the network 2999. Also, in executing thecontrol routine 2540, the processor 2550 may be caused to operate thenetwork interface 2590 to receive the query instructions 2710 from therequesting device 2700, and to relay the query instructions 2710 to atleast a subset of the node devices 2300 as part of coordinating an atleast partially parallel performance of at least a search requested inquery instructions 2710 among at least the subset of the node devices2300. Further, in executing the control routine 2540, the processor 2550may be caused to operate the network interface 2590 to receive portionsof the results of the performance of at least the search requested inthe query instructions 2710, and to transmit the results data 2770generated from the results received from at least the subset of the nodedevices 2300 back to the requesting device 2700.

Alternatively, in embodiments in which one of the node devices 2300incorporate the controller 2503 such that there may not be a controldevice 2500, the controller 2503 may incorporate the processor 2550and/or the storage 2560, and the processor 2550 may operate the networkinterface 2390 of such a node device 2300 in lieu of there being aseparate and distinct network interface for use by the processor 2500.As depicted, in such embodiments, each of the rules data 2510, the nodedata 2539 and the control routine 2540 may be stored within the storage2360 of such a node device 2300 or within the separate storage 2560 ofthe controller 2503.

It should be noted that some embodiments of the distributed processingsystem 2000 of FIG. 13A may include multiple ones of the control device2500, and/or that some embodiments of the distributed processing system2000 of FIG. 13B may include multiple node devices 2300 that may eachinclude the controller 2503. It may be that the provision of multiplecontrol devices 2500 or multiple controllers 2503 serves the purpose ofproviding redundancy in the functions performed thereby in which afailure within one control device 2500 or controller 2503 results in atakeover of the functions performed thereby by another control device2500 or controlled 2503. Alternatively or additionally, in an amalgam ofdistributed processing systems 2000 of FIGS. 13A and 13B, it may be thata node device 2300 incorporates a controller 2503 to take over thefunctions of a separate and distinct control device 2500 should afailure of the control device 2500 occur. Alternatively or additionally,it may be that multiple control devices 2500 and/or multiple controllers2503 are incorporated into an embodiment of the distributed processingsystem 2000 to interact with what may be multiple ones of the requestingdevice 2700 in order to employ some degree of parallel processing in thereceipt and handling of multiple instances of the query instructions2710 received therefrom, each of which may include the search criteriaof an entirely different search to be performed and/or task instructionsof entirely different tasks to be performed with data values retrievedin corresponding searches.

In various embodiments, the requesting device 2700 incorporates one ormore of a processor 2750, a storage 2760, an input device 2720, adisplay 2780, and a network interface 2790 to couple the requestingdevice 2700 to the network 2999. The storage 2760 may store the queryinstructions 2710, the results data 2700 and/or a control routine 2740.The control routine 2740 may incorporate a sequence of instructionsoperative on the processor 2750 to implement logic to perform variousfunctions. The processor 2750 may be caused by its execution of thecontrol routine 2740 to operate the input device 2720 and the display2780 to provide a graphical user interface (GUI), command line interface(CLI) and/or application programming interface (API) by which anoperator may enter parameters of a query for data from the data set 2230that may include a search for particular data that may be present in oneor more of the data records 2133, and may additionally include a task tobe performed with data values retrieved by the search. The processor2750 may then generate the query instructions 2710 to include a set ofmachine-readable instructions based on the entered parameters. However,regardless of the exact manner in which the query instructions 2710 aregenerated, the processor 2750 may be caused to operate the networkinterface 2790 to transmit the query instructions to the control device2500. The processor 2750 may also be caused to operate the networkinterface 2790 to receive the results data 2770 providing results of theperformance of the query instructions by at least a subset of the nodedevices 2300. The processor 2750 may be further caused to generate avisual representation of the results data 2700, and then operate thedisplay 2780 to visually present the visual representation.Alternatively, the processor 2750 may be caused to relay the resultsdata 2700 or other representation of the results indicated in theresults data to another routine (not shown) for use as an input forfurther processing.

It should be noted that some embodiments of the distributed processingsystem 2000 of FIG. 13A or FIG. 13B may include multiple ones of therequesting device 2700. It may be that an embodiment of the distributedprocessing system 2000 of either FIG. 13A or FIG. 13B is shared among agreat many institutions and/or personnel thereof, and therefore, mayroutine perform numerous searches and/or other processing tasks at leastpartially in parallel in response to the receipt numerous instances ofthe query instructions 2710. As earlier discussed, such an embodiment ofthe distributed processing system 2000 of either FIG. 13A or FIG. 13Bmay incorporate multiple ones of the control device 2500 and/or of thecontroller 2503 incorporated into multiple ones of the node devices 2300to more efficiently receive such multiple instances of the queryinstructions 2710, and to more efficiently control the performances ofsearches and/or other processing tasks in response.

FIG. 14A depicts various aspects of an example embodiment ofdistribution of the data set 2230 among multiple ones of the nodedevices 2300. As depicted, each node device 2300 may store one or morewhole super cells 2233 of the data set. More specifically, and asdepicted among a number of node devices 2300 a through 2300 x, nodedevice 2300 a may be provided with at least one whole super cell 2233 a,node device 2300 b may be provided with at least one whole super cell2233 b, and so on to node device 2300 x that may be provided with atleast one whole super cell 2233 x. Such use of the super cells 2233 asthe atomic unit of distribution of the data set 2230 among the nodedevices 2300 may be deemed necessary to avoid situations in which asuper cell index 2430 that corresponds to a super cell 2233 may need tobe shared among two or more node devices 2300, such that the variouscomplexities of either sharing access to a single copy of such a cellindex 2430 or of recurringly synchronizing multiple copies thereof amongthe two or more node devices 2300 may need to be incurred.

In some embodiments, the manner in which the data set 2230 is dividedinto the super cells 2233 may be based on a requirement to generate allof the super cells 2233 to be of relatively similar sizes, as part of aneffort to distribute the use of storage, processing and/or otherresources by the data set 2230 relatively evenly among the node devices2300. Alternatively or additionally, the manner in which the data set2230 is divided into the super cells 2233 may be reflective of adivision of the data values of the data set 2230 based on a need orrequirement for various subsets of the data records 2133 of the data set2230 to be maintained together within a single node device 2300 toenable the performance of various operations therewith. As a result ofthe division of the data set 2230 based on characteristics of the datavalues, rather than based on a requirement to generate similar sizedsuper cells 2233, super cells 2233 of greatly varying sizes may begenerated, including one or more super cells 2233 that may not bestorable within faster volatile storage (e.g., random access memory) ofone or more of the node devices 2300. However, as will be familiar tothose skilled in the art, as long as a super cell 2233 that is providedto and/or generated within a node device 2300 is able to be stored inits entirety within slower non-volatile storage (e.g., a hard diskdrive) of the node device, then any of a variety of virtual memoryalgorithms may be employed to cause swapping of portions of such a supercell 2233 between the volatile and not-volatile storages of the nodedevice 2300 as a way to overcome such limitations.

As also depicted, each one of the super cells 2233 a through 2233 x maybe accompanied by its corresponding one of the super cell indexes 2430 athrough 2430 x in being stored within the corresponding one of the nodedevices 2300 a through 2300 x. Accordingly, each data cell 2130 of oneof the super cells 2233 a through 2233 x may be accompanied by acorresponding cell index 2330. As previously discussed, this may be dueto the data set 2230 actually incorporating the data set index 2530 insome embodiments.

FIG. 14B depicts further aspects of the organization of the data set2230 and of the data set index 2530 when stored together as a data file2110 by the one or more data devices 2100. As familiar to those skilledin the art, the head of the data file 2110 may begin with a file header2111 that may contain various pieces of information about the data file2110 as may be required by a file system employed by the one or moredata devices 2100 to store and maintain files. Following the file header2111 may be a file data payload 2113 in which, in accordance withvarious requirements of the file system employed by the one or more datadevices 2100, may be the portion of the data file 2110 in which the dataof the data file is contained. As depicted, super cells 2233 of the dataset 2230 may be interspersed within the file data payload 2113 of thedata file 2110 with corresponding super cell indexes 2430 and cellindexes 2330. More specifically, starting toward the head of the filedata payload 2113, the one or more data cells 2130 that form a supercell 2233 may be positioned adjacent to each other in sequential order,followed by the super cell index 2430 for the super cell 2233. The supercell index 2430 may then be followed by the one or more cell indexes2330 that correspond to the one or more data cells 2130, which may bepositioned adjacent to each other and in a sequential order that mirrorsthe sequential order of the corresponding one or more data cells 2130.This very same arrangement of the data cells 2130 of a super cell 2233,corresponding super cell index 2430 and corresponding cell indexes 2330may be repeated for each super cell 2233 of the data set 2230 throughoutthe file data payload 2113.

As also depicted, when a portion of the data set 2230 that includes oneor more super cells 2233 is distributed to one of the node devices 2300such that the node device 2300 stores a portion of the data set 2230,the treatment of super cells 2233 as atomic units may be relied upon tocause each super cell 2233 that is transmitted to a node device 2300 tobe accompanied by its corresponding super cell index 2430 andcorresponding one or more cell indexes 2330 in a manner that preservesthis same arrangement among the super cell 2233, the super cell index2430 and the one or more cell indexes 2330 when stored within the nodedevice 2300. As will be familiar to those skilled in the art, the mannerin which each super cell 2233 may be transmitted to a node device 2300may entail providing the node device 2300 with a pointer to the locationof the super cell 2233 within the file data payload 2113 of the datafile 2110 and an indication of an amount of data to be transferred thatmay include the super cell 2233 along with the corresponding super cellindex 2430 and corresponding one or more cell indexes 2330.

Upon being stored within the storage 2360 of the node device 2300, themanner in which the data of each super cell 2233 may be accessed thereinmay still be based upon the use of a pointer to the head of the supercell 2233 along with one or more offsets to locations within the supercell 2233 at which each data cell 2130 of the super cell 2233 may begin.The arrangement of the data cells 2130 of each super cell 2233 to beginat the location pointed to by such a pointer, and with the correspondingsuper cell index 2430 and corresponding one or more cell indexes 2330positioned after the data cells 2130, may present an arrangement that iscompatible with a routine executed within the node device 2300 that maynot have been written to make use of the data set index 2530, at all.Therefore, although the corresponding super cell index 2430 andcorresponding one or more cell indexes 2330 may also be present withinthe storage 2360 such that they could also be accessed using offsetsfrom the same pointer, such a routine may simply never make use of suchoffsets to access these indexes. It may be that such a routine (whichmay be any of a variety of types of routine) was written at an earliertime that predated the practice of including the data set index 2530with the data set 2230. Alternatively or additionally, such a routinemay include any of a variety of data handling utilities, including andnot limited to, data archiving utilities, data summation and reportutilities and/or any of a variety of other utilities that may routinelyaccess the entirety of the data set 2230 such that use of the data setindex 2530 may be superfluous. For such routines, such a groupingtogether of the data cells 2130 of a super cell 2233 starting at thelocation, and proceeding without other types of information interposedbetween the data cells 2130 may reduce memory page swapping and/or otherforms of thrashing by virtual memory management systems. Thus, thepositioning of the data cells 2130 in sequential order starting at thelocation within the storage 2360 to which the pointer associated withthe super cell 2233 points may provide exactly the organization of thedata of the super cell 2233 that such a routine was expected toencounter when it was written, thereby allowing such a routine tosuccessfully make use of the data of the super cell 2233 in the mannerin which it was designed to do so, and without interference by thepresence of the corresponding super cell index 2430 and thecorresponding one or more cell indexes 2330.

FIG. 14C depicts various aspects of an example embodiment oforganization of data values within a single data cell 2130, and variousaspects of the manner in which the data values within a single data cell2130 may be indexed within a single cell index 2330 that corresponds tothe single data cell 2130. As has been discussed, and as depicted, thedata values within the single data cell 2130 may be organized to formmultiple data records 2133, and each data record 2133 may be assigned aunique record identifier 2132, such as the depicted sequence ofascending integer values. Also, each of the data records 2133 within thedepicted single data cell 2130, as well as throughout the entirety ofthe data set 2230, may include an identical quantity of data fields 2134in which a data value may be stored, each of which may be assigned afield label 2136 that is also identical through the entirety of the dataset 2230. Therefore, although the data values within a data cell 2130may not actually be arranged in a manner that forms a two-dimensionalarray in the storage 2360 of a node device 2300, the allocation ofrecord identifiers 2132 and of field labels 2136 may enable data valueswithin the data records 2133 of the data cell 2130 to be accessed in amanner very much like a two-dimensional array. However, it should benoted that, although each data record 2133 throughout the entirety ofthe data set 2230 may include an identical set of data fields 2134,there may still be differing quantities of data records 2133 within eachdata cell 2130 of the data set 2230.

As also depicted, and as will be explained in greater detail, a subsetof the data fields 2134 of the data records 2133 within the data cell2130 may be indexed within the cell index 2330 that corresponds to thedata cell 2130. It is envisioned that only a subset of the data fields2134 will be employed as the basis of search criteria in searches of thedata set 2230 for one or more data records 2133 that meet the searchcriteria. Therefore, for each data field 2134 that may be so employed insuch searches, the cell index 2330 may include at least a unique valuesindex 2334, and may additionally include one or more duplicate valueindexes 2337. As will be explained in greater detail, the unique valuesindex 2334 that corresponds to a particular data field 2134 may includethe record identifiers 2132 of each data record 2133 in which a uniquedata value for the particular data field 2134 is present. Also, anyduplicate value index 2337 that corresponds to the particular data field2134 (if there are any for the particular data field 2134) may includethe record identifier(s) 2132 of each data record 2133 in which aduplicate of one of the unique data values for the particular field 2134is present. Thus, where the data values of the particular data field2134 have such high cardinality that each data value is unique, then thecell index 2330 will not include any duplicate value indexes for theparticular data field 2134.

FIG. 15A illustrates an example of performing a combination ofassembling, indexing, searching, accessing and/or performing processingoperations with the a portion of the data set 2230 stored within one ofthe node devices 2300. More specifically, FIG. 15A illustrates aspectsof the manner in which the routines 2340 and 2540 may be executedcooperatively within embodiments of the distributed processing system2000 of either of FIG. 13A or 13B to generate and/or receive the dataset 2230 and corresponding data set index 2530, and to use the data setindex 2530 to improve the efficiency with which the data set 2230 may besearched and used in the manner indicated in the query instructions2710.

As recognizable to those skilled in the art, each of the controlroutines 2340 and 2540, including the components of which each may becomposed, are selected to be operative on whatever type of processor orprocessors that are selected to implement applicable ones of theprocessors 2350 and/or 2550. In various embodiments, each of theseroutines may include one or more of an operating system, device driversand/or application-level routines (e.g., so-called “software suites”provided on disc media, “applets” obtained from a remote server, etc.).Where an operating system is included, the operating system may be anyof a variety of available operating systems appropriate for execution bythe processors 2350 and/or 2550. Where one or more device drivers areincluded, those device drivers may provide support for any of a varietyof other components, whether hardware or software components, of thenode devices 2300, the control device 2500 and/or the controller 2503.

As depicted in FIG. 15B, the processors 2350 and/or 2550 may incorporatemultiple processor cores 2355 and/or 2555, respectively, and/or othermechanisms by which parallel execution of multiple processes acrossmultiple threads of execution may be supported. As has been discussed,the performance of various operations by the processors 2350 of the nodedevices 2300, and/or by the processor 2550 of the control device 2500 orcontroller 2503, may entail the use of at least some degree ofparallelism within each such device, in addition to or in lieu of theuse of parallelism between devices, to increase speed and/or efficiency.Thus, during execution of one or more of the component 2545 of thecontrol routine 2540, and/or of the components 2342, 2343, 2345, 2346and 2347 of the control routine 2340, operations performed with eachcell index 2330, each data cell 2130, each super cell index 2430 and/oreach super cell 2233 may each be so performed in a separate process thatis distributed among the available multiple threads of execution thatare so supported by the processors 2350 and/or 2550.

Returning to FIG. 15A, as has also been discussed, the control device2500 or controller 2503 may coordinate aspects of exchanges of the dataset 2230 and/or the data set index 2530 between the one or more datadevices 2100 and the node devices 2300, including the manner in whichthe data set 2230 (and correspondingly, the data set index 2530) may bedistributed among multiple ones of the node devices 2300. As depicted,the control routine 2340 executed by the processor 2350 of each nodedevice 2300 may include a status component 2349 to cause the processor2350 of each node device 2300 to operate its corresponding networkinterface 2390 to recurringly transmit indications of the current statusof the node device 2300 to the control device 2500 or controller 2503.As also depicted, the control routine 2540 executed by the processor2550 of the control device 2500 or controller 2503 may include acoordinating component 2549 to cause the processor 2550 to receive suchrecurringly transmitted indications of node status and to recurringlyupdate node data 2539 stored within the control device 2500 orcontroller 2503 with those received indications.

Based on such recurringly updated indications of node status maintainedwithin the node data 2539, the processor 2550 may be caused by thecoordinating component 2549 to control the distribution of the data set2230 and corresponding portions of the data set index 2530 among thenode devices 2300 to balance the utilization of network, storage,processing and/or other resources provided by each of the node devices2300. Alternatively or additionally, the processor 2550 may be caused tocontrol such a distribution based on any of a variety of algorithms bywhich portions of the data set 2230 (and correspondingly, portions ofthe data set index 2530), may be distributed to provide a predetermineddegree of redundancy to avoid loss of data that may otherwise arise froma failure affecting one or more of the node devices 2300. Where suchdistributions are made through the transfer of the entirety of the dataset 2230 from the one or more data devices 2100, and to the node devices2300 along with corresponding portions of the data set index 2530, theprocessor 2550 may be caused to coordinate the transfer of each portionof the data set 2230 to each corresponding node device 2300, along witha corresponding portion of the data set index 2530, in an at leastpartially parallel set of transfer operations through the network 2999.

However, as has also been discussed, it may be that, in otherembodiments, the data set 2230 is assembled by the node devices 2300,and that the data set index 2530 may correspondingly be generated by thenode devices 2300. In such other embodiments, the processor 2550 may becaused by the coordinating component 2549 to determine which one(s) ofthe node devices 2300 are to receive data values and/or stream(s) ofdata values from each data device 2100. Again, such determinations maybe based on recurringly updated indications of node status maintainedwithin the node data 2539, and/or based on any of a variety ofalgorithms by which a predetermined degree of redundancy is to beprovided to avoid loss of data. As depicted, the control routine 2340executed by the processor 2350 of each node device 2300 may include acollection component 2341 to cause the processor 2350 of each nodedevice 2300 to operate its corresponding network interface 2390 torecurringly receive data values and/or streams of data values from theone or more data devices 2100, and to assemble one or more super cells2233 of the data set 2230. FIGS. 16A and 16B each depict aspects of suchreception and assembly of data values and/or stream(s) of data values ingreater detail. FIG. 16A depicts aspects of assembling an exampleportion of a data set 2230 that includes integer data values, and FIG.16B depicts aspects of assembling an example portion of a data set 2230that includes variable length character strings.

Turning to FIG. 16A, in executing the control routine 2340, theprocessor 2350 of a node device 2300 may be caused by the collectioncomponent 2341 to assemble an example data cell 2130 of an example supercell 2233 from received data values. As depicted, the received datavalues may include integer values filling a data field 2134 associatedwith a field label 2136 of “Job_ID” that is present in each of the datarecords 2133 of the example data cell 2130. As also depicted, each ofthe data records 2133 of the example data cell 2130 may be assigned arecord identifier 2132 by which each of the depicted data records 2133is able to be uniquely identified, at least within the example data cell2130. As depicted, the record identifiers so assigned are an ascendingsequential series of positive integers. However, it should be noted thatany of a variety of types of record identifiers may be used.

It should be noted that FIGS. 16A and 16B each present a deliberatelysimplified example of a single data cell 2130 within a super cell 2233of a data set 2230 as a visual aid for discussion and understanding. Itis envisioned that a real data set 2230 would include a great many supercells 2233, that would each include a great many data cells 2130, thatwould each include a great many data records 2133, that would eachinclude a great many data fields 2134. Therefore, the deliberatelysimplified examples presented in FIGS. 16A and 16B, one of which is usedthroughout other figures of the present application, should not be takenas limiting.

In assembling the example data cell 2130 of the depicted example supercell 2233, the processor 2350 may be caused by the collection component2341 to retrieve one or more rules for such assembly from the rules data2510. In some embodiments, the rules data 2510 may be distributed amongthe node devices 2300 by the control device 2500 or the controller 2503.Among such rules may be specifications of what data fields 2134 are tobe included in each data record 2133, what the field labels 2136 anddata types are for each data field 2134, and/or what sort sequence ofrecord identifiers 2132 are to be used. Alternatively or additionally,among such rules may be specifications of minimum and/or maximum size ofeach data cell 2130 and/or each super cell 2233 to be assembled,specifications of minimum and/or maximum quantities of data records 2133to be included within each data cell 2130, and/or specifications ofminimum and/or maximum quantities of data cells 2130 to be includedwithin each super cell 2233. Such parameters for the generation of datacells 2130 and/or super cells 2233 may be based on any of a variety offactors associated with limitations of the network, storage, processingand/or other resources provided by the node devices 2300, and/or suchlimitations associated with other devices to which the data set 2230 maybe transmitted by the node devices 2300 following its generation. Insome embodiments, such parameters may be modified on a recurring basisbased on recurring analyses of performance (e.g., such metrics as timeto perform various operations, utilization of resources, efficiency,data throughput, rate of handling queries, etc.) of the distributedprocessing system 2000, including analyses of the efficiency with whichthe various resources provided by the node devices 2300, the controldevice 2500 (or controller 2503) and/or of the network 2999.

As also depicted, in some embodiments, the collection component 2341 mayincorporate a compression engine, and the processor 2350 may be causedthereby to compress each data cell 2130 generated within the node device2300. In so doing, the processor 2350 may be caused to retrieve one ormore data compression rules from the rules data 2510. Among such rulesmay be specifications of what type of compression algorithm to useand/or parameters therefor.

Turning to FIG. 16B, an alternate deliberately simplified example of asingle data cell 2130 is presented as a visual aid to illustrate anexample indirect manner in which data values of a data field 2134 may bestored. More specifically, the data type of the data associated with thedepicted example data field 2134 is a variable length character string.Due to the variable length, the data size of each data value (i.e., thequantity of bytes, words, doublewords, quadwords, etc. that may beoccupied by each data value) may be entirely unpredictable, and/or maychange over time as changes are made to one or more of the data values.As depicted, in one approach to addressing such issues, each of suchvariable length data values may be separately assembled together to forma data value vector 2135 of characters thereof. In the data field 2134of the data records 2133 into which these variable length characterstrings might otherwise be stored, there may instead be a pair ofinteger values of preselected fixed data size, with one such integervalue specifying an offset from the start of the data value vector 2135at which a corresponding one of the variable length strings begins, andanother such integer value specifying the length of that correspondingone of the variable length strings.

Returning to FIG. 15A, regardless of whether the data cells 2130 andsuper cells 2233 of the data set 2230 are assembled within the nodedevices 2300, or are provided to the node devices 2300 as a completedata set 2230, the node devices 2300 may be caused to generate and/orrepeat the generation of the cell indexes 2330 and super cell indexes2430 of a corresponding data set index 2530. Stated differently, wherethe data set 2230 is generated among multiple ones of the node devices2300 such that the data set 2230 such that no corresponding data setindex 2530 yet exists, each node device 2300 of the multiple nodedevices 2300 that generated a portion of the data set 2230 may alsogenerate a corresponding portion of the data set index 2530. Also, wherethe data set 2230 is provided to multiple ones of the node devices 2300as an already complete data set, but without a corresponding data setindex 2530, each node device 2300 of the multiple node devices 2300 thatis provided with a portion of the already complete data set 2230 maythen generate a corresponding portion of the data set index 2530.Alternatively, where the data set 2230 is provided to multiple ones ofthe node devices 2300 as an already complete data set, and with acorresponding data set index 2530, it may be that none of the nodedevices 2300 need generate the corresponding data set index 2530.However, regardless of whether the data set 2230 and the data set index2530 are each generated by the node devices 2300 or are each provided tothe node devices 2300, if data values within the data set 2230 areadded, deleted or otherwise altered, then one or more of the nodedevices 2300 may be caused to repeat the generation of at least aportion of the data set index 2530.

As depicted, the control routine 2340 executed by the processor 2350 ofeach node device 2300 may include an ordering component 2342 to causethe processor 2350 of each node device 2300 to retrieve and order datavalues of one or more specified data fields 2134, and an indexingcomponent 2343 to cause the processor 2350 of each node device 2300 togenerate cell indexes 2330 and super cell index(es) 2430 based on thosedata values. FIGS. 17A-C, together, depict aspects of such generation ofa portion of the data set index 2530 in greater detail. Specifically,FIG. 17A depicts aspects of the retrieval and ordering of data values ofa data field 2134 to identify unique values and duplicates thereof, FIG.17B depicts aspects of generating unique and duplicate value indexes ofa cell index 2330 from such retrieved and ordered data values, and FIG.17C depicts aspects of adding indications of highest and lowest datavalues to the cell index 2330 and a super cell index 2430.

Turning to FIG. 17A, in executing the control routine 2340 to begingenerating or repeating the generation of a portion of the data setindex 2530, the processor 2350 of a node device 2300 may be caused bythe ordering component 2342 to retrieve indications from the rules data2510 of what data fields 2134 are within the subset of data fields 2134for which indexes are to be created. It is envisioned that the datavalues of a relatively small subset of the data fields 2134 may actuallybe used in the search criteria of searches for data that may bespecified in instances of the query instructions 2710. Exactly whichdata fields 2134 are likely to be so used may be based on a combinationof factors including, and not limited to, the nature of the data withinthe data set 2230 and/or the purposes for which the data set 2230 iscreated. Thus, it is envisioned that a determination of which datafields 2134 are to be included in the subset of data fields 2134 forwhich indexes are to be generated may be objectively made through theobservation of what data fields 2134 are used in search criteriaspecified in instances of the query instructions 2710 that are receivedover time. Therefore, in some embodiments, the processor 2350 of eachnode device 2300 may be caused to retrieve explicit indications of whatdata fields 2134 are to be included in the subset from the rules data2510, and such explicit indications may be based on past analyses ofpast queries that may be performed by the control device 2500 orcontroller 2503. Alternatively or additionally, the processor 2350 ofeach node device 2300 may be caused to retrieve one or more rules (e.g.,statistical analysis rules and/or heuristic algorithms) for use by theprocessor 2350 in performing its own analysis of received instances ofthe query instructions 2710 to determine what the subset of data fields2134 should be over a specified interval of time into the past.

Regardless of the exact manner in which the subset of data fields 2134for which indexes are to be generated is determined, the processor 2350of each node device 2300 may be caused by the ordering component 2342 tocommence or repeat generation of cell indexes 2330 and super cellindex(es) 2430 for each data cell 2130 of each super cell 2233 that isstored within the node device. As previously discussed, as a measure toimprove the speed and/or efficiency of such generation or repetition ofgeneration of indexes, the processor 2350 may be selected to be of atype that is capable of supporting multiple threads of execution (e.g.,incorporating multiple processor cores 2355), and the generation of eachcell index 2330 and/or each super cell index 2430 may be performed as aseparate process, with the separate processes being distributed amongthe multiple threads of execution supported by the processor 2350.

Within each such process for the generation or repetition of generationof one of the cell indexes 2330, the processor 2350 may be caused toretrieve the data values present within the each data field 2134 of thesubset of data fields 2134 from within each data record 2133 of the datacell 2130 that corresponds to the cell index 2330. As another measure toimprove speed and/or efficiency within each process for generating orrepeating the generation of a cell index 2330, the processor 2350 may becaused to retrieve the data values within all of the data fields 2134 ofthe subset of data fields 2134 in a single read pass through the datarecords 2133 within the corresponding data cell 2130. For each datafield 2134 of the subset of data fields 2134, various approaches may beused to sort the data values retrieved therefrom across the data records2133 within the corresponding data cell 2130, as well as to distinguishunique and duplicate values thereof. Although FIG. 17A depicts the useof a binary tree (specifically, the depicted unique values ordering tree2314) as one approach, other approaches may be used, including and notlimited to, a multi-level skip list.

It should be noted that FIG. 17A presents a deliberately simplifiedexample of indexing the data values of a single data field 2134 of thesimplified example data cell first introduced in FIG. 16A as a visualaid for discussion and understanding. Again, it is envisioned that realdata cells 2130 of a real data set 2230 would include a great many datarecords 2133 that each would include a great many data fields 2134 ofwhich numerous ones would be indexed. Therefore, the deliberatelysimplified example of indexing presented in FIG. 17A, and continuingwith FIGS. 17B and 17C, should not be taken as limiting.

Continuing with FIG. 17A, the depicted unique values ordering tree 2314is assembled from the unique data values that are identified as presentwithin a single example data field 2134 as those values are retrieved,while the depicted duplicate values table 2317 is assembled from anyduplicates of any of the unique data values that are identified aspresent. More specifically, as each data value present within the singleexample data field 2134 is retrieved from the data records 2133 of thedepicted data cell 2130 (starting with the data record 2133 assigned therecord identifier “0”), the retrieved data value is compared to the datavalues that may already be present within the unique values orderingtree 2314 to determine whether the retrieved data value is alreadyincluded within the unique values ordering tree 2314. If the retrieveddata value is not already included in the unique values ordering tree2314, then it is deemed to be a “unique value” in the sense that it isthe first time that the retrieved data value has been encountered, andthe retrieved data value is added to the unique values ordering tree2314 along with the record identifier 2132 of the data record 2133 fromwhich it was retrieved.

However, if the retrieved data value is already included in the uniquevalues ordering tree 2314, then it is deemed to be a “duplicate value”in the sense that it is not the first time that the retrieved data valuehas been encountered, and an indication is added to the duplicate valuestable 2317 that a duplicate of one of unique values has been retrieved,along with the record identifier 2132 of the data record 2133 from whichit was retrieved. As depicted, the duplicate values table 2317 may beindexed by the record identifiers 2132 of the data records 2133 in whichunique values are identified as present, and for each such recordidentifier 2132 associated with a unique value, there may be a count ofduplicates of that unique value (if any), and a listing of recordidentifier(s) of any data record 2133 in which a duplicate of thatunique value has been identified as present (again, if any). Again, dueto the deliberate simplified nature of the depicted example data cell2130, just a single duplicate is depicted for each of two of the uniquevalues. However, it is envisioned that in a real data cell 2130, theremay be numerous unique values that may each have multiple duplicatessuch that many of the counts of duplicates in the duplicate values table2317 would be higher for those unique values, and such that there wouldbe many record identifiers 2132 for each of those unique values in theduplicate values table 2317 to identify multiple data records 2133 inwhich a duplicate value is identified as present for each of thoseunique values. It should be noted that, although a specificconfiguration of a data table is depicted and described as an exampleimplementation of the duplicate values table 2317, other configurationsof a data table and/or an entirely different data structure may be usedto store indications of duplicate values correlated to the unique valuesof which they are duplicates and correlated to indications of which datarecord(s) 2133 that the duplicate values are identified as presentwithin. Further, as an alternative, entirely separate data structuresmay be generated for each unique value for which at least one duplicateis identified. More broadly, any of a variety of data structures and/orother techniques may be employed to store such indications of duplicatevalues in a form optimized for compact storage and/or in a formoptimized for speedy access.

In generating the depicted unique values ordering tree 2314 and thedepicted duplicate values table 2317, the processor 2350 may be causedto retrieve various rules for the ordering of data values from the rulesdata 2510. Such retrieved rules may specify particular forms of orderingdata values for each of multiple data types, including and not limitedto, numeric ordering rules for integer and/or floating point values(e.g., ascending or descending numerical order), text character orderingrules for fixed length and/or variable length strings of text (e.g.,alphabetic ordering, one or more exceptions to alphabetic ordering,ordering for different dialects and/or other variants of a language,etc.), and/or ordering rules for audio and/or visual data (e.g.,ordering by color, pixel resolution, sound frequency, sound volume,etc.). Such rules may also specify aspects of the generation of thebinary tree structure of the unique values ordering tree 2314.Alternative or additionally, where an approach other than a binary treeis used to sort data values and/or distinguish unique and duplicatevalues (e.g., a multi-layer skip list), such rules may also specifyaspects of the use of such another approach.

As also depicted, in some embodiments, the ordering component 2342 mayincorporate a decompression engine, and the processor 2350 may be causedthereby to decompress the data cell 2130 in a situation where the datacell 2130 was earlier compressed as part of being generated (e.g., bythe collection component 2341, which as earlier discussed, may include acompression engine). In so doing, the processor 2350 may be caused toretrieve one or more data decompression rules from the rules data 2510.Among such rules may be specifications of what type of decompressionalgorithm to use and/or parameters therefor.

Turning to FIG. 17B, in executing the control routine 2340 to continuegenerating or repeating the generation of a portion of the data setindex 2530, the processor 2350 of a node device 2300 may be caused bythe indexing component 2343 to employ the unique values ordering tree2314 and/or the duplicate values table 2317 (or alternates thereto, ashave been described) to generate a cell index 2330 that corresponds todata cell 2130. More specifically, for purposes of illustration, thegeneration of an example cell index 2330 based on the unique valuesordering tree 2314 and the duplicate values table 2317 generated fromthe example data cell 2130 of FIG. 17A is depicted.

The processor 2350 may be caused by the indexing component 2343 toperform an in-order traversal of the unique values ordering tree 2314(which again, employs a binary tree structure) to retrieve at least therecord identifiers 2132 of the data records 2133 in which each uniquevalue was identified as present for the single data field 2134, and inan order that corresponds to the order into which the unique values aresorted in the unique values ordering tree 2314. Then, within the cellindex 2330, the processor 2350 may be caused to generate a unique valuesindex 2334 for the unique values of the single data record 2134, whichmay include a count of the unique values and/or the retrieved recordidentifiers 2132 arranged in the order that corresponds to the orderinto which the unique values are sorted in the unique values orderingtree 2314. As depicted, in some embodiments, in performing the in-ordertraversal of the unique values ordering tree 2314, the processor 2350may also retrieve the unique values, themselves, and also in the orderinto which they are sorted in the unique values ordering tree 2314. Theprocessor 2350 may then generate and add a unique values vector 2335 tothe unique values index 2334 in which the unique values are arranged inthe order into which they are sorted in the unique values ordering tree2314 such that the unique values index 2334 is caused to correlate eachof the unique values with the record identifier 2132 of the data records2133 in which each was identified as present.

Thus, when fully generated, the unique values index 2334 at leastprovides a count of how many unique values were found among the datavalues present within a single data field 2134 across the data records2133 of the single data cell 2130, and/or the record identifiers 2132 ofthe data records 2133 in which each of the unique values were identifiedas present in the order into which the unique values were sorted in theunique values ordering tree 2314. Also, the unique values index 2334 mayadditionally provide a unique values vector 2335 of the unique values,themselves, also in the order into which the unique values were sortedin the unique values ordering tree 2314, thereby allowing each uniquevalue to be correlated to the record identifier 2132 of the data record2133 in which the unique value was identified as present.

The processor 2350 may be caused by the indexing component 2343 to parsethrough the duplicate values table 2317 to retrieve the recordidentifier 2132 for each unique value for which at least one duplicatewas also identified, and the record identifier(s) 2132 for the one ormore data records in which the at least one duplicate was identified aspresent. Then, within the cell index 2330, and for each unique value forwhich at least one duplicate was also identified, the processor 2350 maybe caused to generate a separate duplicate value index 2337. Within eachsuch duplicate value index 2337, the processor 2350 may include therecord identifier 2132 of the data record 2133 in which one of theunique value was identified as present, a count of how many duplicateswere identified of the unique value, and the record identifier(s) of theone or more data records 2133 in which a duplicate of the unique valuewas identified as present.

Thus, when fully generated, each duplicate value index 2337 identifiesthe unique value to which it corresponds by the record identifiers 2132,a count of the number of duplicates of the unique value, and the recordidentifiers 2132 of the data records 2133 in which each duplicate of theunique value was identified as present. The record identifier 2132 ofthe data record in which the unique value was identified as present maybe used with the unique values index 2334 to identify the unique value,itself, in embodiments in which the unique values index 2334 includesthe unique values vector 2335.

Turning to FIG. 17C, in continuing to execute the control routine 2340to continue generating or repeating the generation of a portion of thedata set index 2530, the processor 2350 of a node device 2300 may becaused by the indexing component 2343 to additionally employ the uniquevalues ordering tree 2314 to identify the highest and lowest uniquevalues identified for the single data field 2134 within the cell index2130. The processor 2350 may then add, to the cell index 2330, anindication of a field value range 2338 for the single data field 2134 inwhich the range of the data values identified as present within thesinge data field 2134 is indicated using the identified lowest andhighest data values.

As previously discussed, the generation of each cell index 2330 and eachsuper cell index 2430 within a node device may be caused to be performedwithin a separate process with such processes distributed among multiplethreads of execution supported by the processor 2350. However, each suchprocess that is instantiated for the generation of a super cell index2430 may have dependencies on the generation of the cell indexes 2330for all of the data cells 2130 that are included in the super cell 2233that corresponds to the super cell index 2430. More specifically, insome embodiments, each super cell index 2430 may include copies of thefield value ranges 2338 generated within each of the corresponding cellindexes 2330, such that the super cell index 2430 cannot be completeduntil at least the field value range 2338 has been generated within eachof the corresponding cell indexes 2330. Alternatively, in otherembodiments, each super cell index 2430 may include a super cell fieldvalue range 2438 that specifies the highest and lowest values identifiedas present within a single data field 2134 across the data records 2133of all of the data cells 2130 of the super cell 2233 to which the supercell index 2430 corresponds. In such other embodiments, the super cellfield value range 2438 may be generated from the field value ranges 2338of each of the corresponding cell indexes 2330, such that again, thesuper cell index 2430 cannot be completed until at least the field valuerange 2338 has been generated within each of the corresponding cellindexes 2330.

In embodiments in which the super cell index 2430 is to include a supercell field value range 2438 for a data field 2134, the highest andlowest data values indicated by the field value range 2338 for that datafield 2134 from each corresponding cell index 2330 may be added to adata structure that may be used to sort such values, such as anotherbinary tree. Again, other approaches to sorting such values, includingand not limited to, another multi-layer skip list. Following suchsorting of such values, the lowest and highest of the sorted values maybe identified by the processor 2350 and used to generate the super cellfield value range 2438, which uses those highest and lowest data valuesto specify the range of data values across all data cells 2130 withinthe corresponding super cell 2233.

Turning to FIG. 18, an alternate example of generating indexes from thedata values of a single data field 2134 of the data records 2133 ofanother deliberately simplified example of a single data cell 2130 ispresented to illustrate an example in which there is more than one rulefor sorting the data values. Depicted is an alternate variant of thesimplified data cell 2130 of FIG. 16B in which, for sake of simplicityof depiction, the character strings are stored directly in the singledata field 2134, instead of within a data value vector 2135. As alsodepicted, instead of a single unique values ordering tree 2314 beinggenerated from the data values in accordance with one or more rules forsorting data values for such a single unique values ordering tree 2314,a pair of unique values ordering trees 2314 a and 2314 b are depicted asgenerated in accordance with differing rules specifying two differentapproaches to sorting data values. Such rules may include the use ofregular expressions and/or other means of flexibly identifying, parsingand/or matching patterns of characters within character strings toselect one or more other rules to effect sorting. In this depictedexample, the two different rules or sets of rules for parsing andsorting the data values are based on a difference in alphabeticalsorting of names in which there is sorting by first name (i.e., by namesof individual members of a family) in the unique values ordering tree2314 a and sorting by last name (i.e., by family names) in the uniquevalues ordering tree 2314 b. However, such an instance of multiplediffering approaches to sorting data values may occur in any of avariety of contexts in which there may be differences in sorting basedon such factors including, but not limited to, different cultural normsin the sorting of names (i.e., differences in linguistic collation),differences between languages in alphabetizing similar characters,differences in sorting portions of mathematical equations acrossdifferent cultures and/or technical fields (i.e., differences betweenpractices between two industries, or between an industry and academia),etc.

Although not specifically depicted, each of the two example unique valueordering trees may serve as the basis for generating correspondingseparate unique value indexes. However, the both of the separate uniquevalues ordering trees 2314 a and 2314 b may be accompanied by a singleshared duplicate values table, since differences in rules for sortingwould not affect the identification and the generation of indexing ofany duplicate values. As a result, a single shared set of one or moreduplicate value indexes may be generated.

FIGS. 19A-C, taken together, depict another alternate example ofgenerating indexes from the data values of a single data field 2134 ofthe data records 2133 of another deliberately simplified example of asingle data cell 2130 in greater detail. Depicted is a simplified datacell 2130 in which another example of variable length character stringsare stored. While the simplified examples of FIGS. 16B and 18 were ofnames, the simplified example of FIGS. 19A-C is of universal resourcelocators (URLs) of webpages. Like the example of FIG. 18, for sake ofsimplicity of depiction, the character strings are stored directly inthe single data field 2134, instead of within a data value vector 2135.However, it is envisioned that, in the case of real data values thatinclude character strings, especially long and/or variable lengthcharacter strings, such indirect storage as depicted in FIG. 16B may bedeemed desirable. While FIG. 19A provides the depiction of thissimplified data cell 2130, FIGS. 19B and 19C depict various aspects ofthe generation of a corresponding cell index 2330 and a super cell index2430 therefrom.

Turning to FIG. 19B, in a manner similar to what was depicted in FIGS.17B and 17C, the corresponding cell index 2330 has been generated toinclude a unique values index 2334 and a field value range 2338. Forsake of simplicity of presentation, a depiction of the intervening stepof sorting the data values and distinguishing unique data values fromduplicate data values has been omitted, as such a step may besubstantially similar to what was earlier described in reference to FIG.17A. Also for sake of simplicity of presentation, no duplicate valueindexes 2337 are depicted.

In one departure from what was depicted in the earlier example of FIG.17B, the unique values vector 2335 has been replaced with a hash valuesvector 2336 in which hashes generated from the unique values arearranged in an order that is based on a sorting the hash values into anumerical order (e.g., an ascending order of binary, decimal,hexadecimal, etc., values), and not based on the order into which theunique values among the data values would have been sorted using abinary tree or other approach. As depicted, the indexing component 2343may include a hash engine to generate a hash value from each uniquevalue among the data values identified within the single data field 2134depicted in FIG. 19A.

The generation of hash values may, in some embodiments, be prompted bythe fact that the data values are of variable length and/or areotherwise of large data size, while the hash values generated therefromare able to be defined to have a preselected and relatively small datasize, thereby making the hash values more amenable for use in generatinga more compact form of vector, such as the hash values vector 2336.However, as will be familiar to those skilled in the art, in generatinghash values from the data values, there is a possibility of collisionsamong the hash values in which there may be one or more instances inwhich two or more of the hash values are identical, despite having beengenerated from what may be considerably different data values that mayalso be of considerably different lengths. In recognition of this,duplicate hash values may not be included in the hash values vector2336, such that the quantity of hash values in the hash values vector2336 may be less than the quantity of unique data values. In recognitionof this, and as depicted, the unique values index 2334 may includeseparate counts of unique values and hash values. Also, as a result ofsuch differences in counts, as well as a result of the fact that thehash values are sorted differently from the unique values, the hashvalues within the hash values vector 2334 cannot be correlated to therecord identifiers 2132 of the data records 2133 in which each of theunique values was identified as present within the single data field2134, unlike the unique values within the unique values vector 2335 inthe example of FIG. 17B. Instead, as will be explained in greaterdetail, the primary value of the hash values vector 2336 may be as anaid in determining whether the corresponding data cell 2130 includesdata records 2133 that satisfy search criteria specified in an instanceof the query instructions 2710.

As further depicted in FIG. 19B, the cell index 2330 may additionallyinclude a field hash value range 2339 in which the range of the hashvalues generated from the unique values is specified with the highestand lowest hash values so generated. Turning to FIG. 19C, similar towhat was depicted in FIG. 17C, the super cell index 2430 thatcorresponds to the super cell 2233 that includes the example data cell2130 of FIG. 17A may include either the field value ranges 2338 fromeach of the corresponding cell indexes 2330 or a super cell field valuerange 2438. However, as also depicted in FIG. 19C, the super cell index2430 may additionally include either the field hash value ranges 2339from each of the corresponding cell indexes 2330 or a super cell fieldhash value range 2439 that may be derived from such field hash valuesranges 2339 in a manner very much like the derivation of the super cellfield value range 2438 from such field value ranges 2338. As will beexplained in greater detail, the super cell field hash value range 2439and/or the field hash value ranges 2339 may be used in identifyingcandidate super cells 2233 and/or candidate data cells 2130 in whichthere may be one or more data records 2130 that meet search criteriaspecified in an instance of the query instructions 2710.

Returning to FIG. 15A, with cell indexes 2330 and super cell index(es)2430 generated within each of multiple node devices 2300 to correspondto the data cells 2130 and super cell(s) 2233, respectively, storedwithin each of those multiple node devices 2300, a complete data setindex 2530 now exists and is distributed among those multiple nodedevices 2300 along with the data set 2230, thereby enabling distributedsearching for and accessing of data within the data set 2230. As willshortly be explained in greater detail, in some embodiments, the controldevice 2500 or controller 2503 (along with the node devices 2300) may beinvolved in using the data set index 2530 to identify candidate supercells 2233 in which there may be data that meets search criteriaspecified in an instance of the query instructions 2710. In suchembodiments, the processors 2350 of each of the node devices 2300 thathas generated one or more super cell indexes 2430 may operate theircorresponding network interfaces 2390 to transmit those generated supercell indexes 2430 to the control device 2500 or controller 2503.However, in other embodiments, the control device 2500 or controller2503 may play no role in identifying candidate super cells 2233, andinstead, such a function may be performed by the node devices 2300. Insuch other embodiments, no such transmission of super cell indexes 2430may occur.

FIGS. 20A-F, taken together, depict an example of using super cellindexes 2430 and cell indexes 2330 of a data set index 2530 distributedamong multiple node devices 2300 as part of performing a search of adata set 2230 also distributed among the multiple node devices 2300.Specifically, FIGS. 20A and 20B, taken together, depict aspects of theuse of search criteria with portions of the data set index 2530 toidentify candidate super cells 2233 and candidate data cells 2130 inwhich there may be data records 2133 that meet the search criteria.FIGS. 20C, 20D and 20E, taken together, depict aspects of the use of thesearch criteria with other portions of the data set index 2530 to searchfor data records 2133 within candidate data cell(s) 2130 that meet thesearch criteria.

Turning to FIG. 20A, in executing the control routine 2540, theprocessor 2550 of the control device 2500 or controller 2503 may becaused by the query component 2545 to receive an instance of the queryinstructions 2710 from the requesting device 2700. As has beendiscussed, the query instructions 2710 may include specifications ofsearch criteria to be used in performing a search for data that meetsthe search criteria within the data set 2230. More specifically, thequery instructions 2710 may set forth one or more specific data valuesand/or a range of data values as being required to be present withineach of one or more specific data fields 2134 of a data record 2133 forthat data record 2133 to be deemed as meeting the search criteria. Moreprecisely, for a data field 2134 that is included within the searchcriteria, the query instructions 2710 may specify a range of data valuesthat a data value within the data field 2134 of a data record 2133 mustfall within for that data record 2133 to meet the search criteria forthat data field. Additionally, the query instructions 2710 may specifymultiple discrete data values that a data value within the data field2134 of a data record 2133 must match one of for that data record 2133to meet the search criteria for that data field. Or as anotheralternative, the query instructions 2710 may specify a single data valuethat must be matched by a data value within the data field 2134 of adata record 2133 for that data record 2133 to meet the search criteriafor that data field. In various embodiments, the query instructions 2710may so indicate such ranges of data values, such multitudes of discretedata values and/or such single data values as search criteria for eachdata field 2134 included in the search criteria using any of a varietyof scripting language, database language, etc. By way of example, wheremultiple discrete data values are listed using the SQL databaselanguage, a WHERE IN clause may be used. The query instructions 2710and/or rules data 2510 may also include regular expressions and/or othermeans of flexibly matching patterns within character strings that may beapplied to values specified in the search criteria for the purpose ofrefining the index search.

It is envisioned that the search criteria for a search specified in thequery instructions 2710 may frequently be specified as a logical AND ofsearch criteria specified for each included data field 2134. However, itshould be noted that the search criteria for a search may be specifiedas search criteria for each included data field 2134 that are combinedusing one or more other logical operators in lieu of or in addition to alogical AND. By way of example, an instance of the query instructions2710 may specify the search criteria as a logical OR of search criteriafor two data fields 2134 such that a data record 2133 may be deemed asmeeting the search criteria if one data field 2134 includes a data valuethat matches a specified data value or falls within a specified range ofdata values for that data field 2134, OR if another data field 2134includes a data value that matches another specified data value or fallswithin another specified range of data values.

In some embodiments, an instance of the query instructions 2710 that areprovided to the control device 2500 or controller 2503 may simply berelayed to the node devices 2300. In such embodiments, the processor2550 of the control device 2500 or controller 2503 may not make anydeterminations of which ones of the node devices 2300 are to be providedwith the query instructions 2710, and/or which are not to be soprovided, such that the processing, storage, network and/or otherresources of each node devices 2300 may be utilized in searching fordata records 2133 that meet the search criteria.

However, as has been discussed, in some embodiments, the manner in whichthe data set 2230 and the corresponding data set index 2530 may bedistributed among the node devices 2300 may include the distribution ofredundant copies of the super cells 2233 among the node devices 2300 toavoid loss of data in the event of a failure occurring within one ormore of the node devices 2300. Alternatively or additionally, there maybe other reasons for the distribution of multiple copies of each supercell 2233 among the node devices 2300, including and not limited to,enabling greater parallelism and/or more efficient load balancing bycreating one or more identical side-by-side sets of node devices 2300 towhich the same super cells 2233 are distributed. Therefore, it may notbe necessary to relay the query instructions 2710 to all of the nodedevices 2300 to which one or more of the super cells 2233 have beendistributed to ensure that a search specified in the query instructionsis performed with the entirety of the data set 2230. In suchembodiments, it may be deemed desirable to engage less than all of suchnode devices 2300 in such a search, as part of an approach to loadbalancing of multiple searches among the node devices 2300 and/or aspart of avoiding instances of inefficiency in which the same super cell2233 is being engaged in the same search across multiple node devices2300.

As has been discussed, indications of which super cell 2233 of the dataset 2230 have been distributed to which node devices 2300, including anyredundant copies, may be stored as part of the node data 2539.Therefore, as part of selecting node devices 2300 to be the ones towhich an instance of the query instructions 2710 is to be relayed so asto become involved in performing a search specified therein, theprocessor 2550 may be caused to retrieve and use such indications fromthe node data 2539, as well as indications therefrom of which nodedevices 2300 may be known to be currently unavailable, due to any of avariety of possible situations, including and not limited to, an errorcondition, having its resources currently fully engaged, or being takenoffline for maintenance or other purposes (e.g., as part of animplementation of an elastic grid of the node devices 2300). Upon makingsuch selections, the processor 2550 may then be caused to relay theinstance of the query instructions 2710 to the selected ones of the nodedevices 2300, as depicted in FIG. 20A, which depicts the relaying ofquery instructions 2710 to node devices 2300 a, b, e and g, but not tonode devices 2300 c, d and f.

Alternatively or additionally, in embodiments in which the controldevice 2500 or controller 2503 is utilized to identify candidate supercells that may each include at least one data cell 2130 that may includeat least one data record 2133 that meets the search criteria, theprocessor 2550 may be caused to employ determinations of which supercells 2233 are identified as candidate super cells as a factor inselecting node devices 2300 to which to relay an instance of the queryinstructions 2710. More specifically, the processor 2550 may be causedby the query component 2545 to compare the specified data values and/orspecified ranges of data values for one or more specified data fields2134 that define the search criteria to the super cell field value range2438 and/or the field value ranges 2338 included in each super cellindex 2430 received from the node devices 2300. For each data field 2134included in the search criteria, and which has been indexed such that atleast a range of data values is provided in a super cell index 2430, ifsuch a comparison shows at least some degree of overlap betweenspecified data value(s) and/or specified range(s) of data values of thesearch criteria and corresponding range(s) of data values indicated inthe super cell index 2430 for either the entire corresponding super cell2233 or a data cell 2130 within the corresponding super cell 2233, thenthe corresponding super cell 2233 may be identified by the processor2550 as a candidate super cell.

It should be noted that the manner in which candidate super cells areidentified is affected by the logical operator(s) used in the searchcriteria. By way of example, where the search criteria combines all ofthe individual search criteria for each of the included data fields 2134with a logical AND, then a super cell 2233 can only be a candidate supercell if the individual search criteria are met for all of the datafields 2134 that are included in the search criteria and for which thereis index information present in the corresponding super cell index 2430.In contrast, and by way of another example, where the search criteriacombines all of the individual search criteria for each of the includeddata fields 2134 with a logical OR, then a super cell 2233 can be acandidate super cell if any individual search criteria is met for any ofthe data fields 2134 that are included in the search criteria and forwhich there is index information present in the corresponding super cellindex 2430. Indeed, where the individual search criteria for each of theinclude data fields 2134 are combined in such a manner with a logicalOR, the analysis using the search criteria and the index informationwithin a super cell index 2430 to determine whether the correspondingsuper cell 2233 is a candidate super cell can be ended once any of theindividual search criteria for any one of the include data fields 2134is determined to be met.

As previously discussed, as a measure to improve the speed and/orefficiency of such comparisons, the processor 2550 may be selected to beof a type that is capable of supporting multiple threads of execution(e.g., incorporating multiple processor cores 2555), and the performanceof such comparisons with the super cell field value range 2438 and/orthe field value ranges 2338 of each super cell index 2430 may beperformed in a separate process, with the separate processes beingdistributed among the multiple threads of execution supported by theprocessor 2550. Thus, the determination of whether each of the supercells 2233 of the data set 2230 is a candidate super cell may be made inseparate processes. For each such process that ends with a determinationthat the corresponding super cell 2233 is identified as a candidatesuper cell, the processor 2550 may be caused to relay the instance ofthe query instructions 2710 to a node device 2300 that stores thatidentified candidate super cell 2233, along with an indication of whichsuper cell 2233 was identified as a candidate super cell. Otherwise, foreach such process that ends with a determination that the correspondingsuper cell 2233 is not identified as a candidate super cell, theprocessor 2550 may not be caused by that process to so relay an instanceof the query instructions. In this way, determinations of whether or noteach super cell 2233 is a candidate super cell may be made at leastpartially in parallel, and without interprocess dependencies.

Turning to FIG. 20B, an example one of the node devices 2300 to whichthe instance of the query instructions 2710 received by the controldevice 2500 or controller 2503 has been relayed is depicted as storingexample super cells 2233 a and 2233 b that each include only two datacells 2130. It should be noted that the example super cells 2233 a and2233 b are deliberately simplified examples of super cells 2233 that arepresented in FIG. 20B to illustrate an example identification of acandidate super cell 2233 and of a candidate data cell 2130. As has beendiscussed, it is envisioned that real super cells 2233 of a real dataset 2230 would include numerous data cells 2130.

In embodiments in which the control device 2500 or controller 2503 doesnot perform the task of identifying candidate super cells 2233, theprocessor 2350 of each of the node devices 2300 to which the instance ofthe query instructions 2710 is relayed is caused to perform thatfunction by the selection component 2345. More specifically, theprocessor 2350 of the example node device 2300 may be caused to comparethe specified data values and/or specified ranges of data values for oneor more specified data fields 2134 that define the search criteria inthe query instructions 2710 to the super cell field value range 2438 aand/or the field value ranges 2338 aa and 2338 ab included in the supercell index 2430 a, and to the super cell field value range 2438 b and/orthe field value ranges 2338 ba and 2338 bb included in the super cellindex 2430 b that correspond to each of the one or more specified datafields. As depicted, such comparisons lead to the super cell 2233 abeing identified as a candidate super cell 2233 that may include acandidate data cell 2130 that may include one or more data records 2133that may meet the search criteria, while the super cell 2233 b is notidentified as a candidate super cell.

However, in embodiments in which the control device 2500 or controller2503 does perform the task of identifying candidate super cells 2233,then the act of relaying the instance of the query instructions to theexample node device 2300 may serve as an indication that at least one ofthe super cells 2233 a and 2233 b has been identified as a candidatesuper cell. As earlier discussed, the control device 2500 or controller2503 may also transmit an indication to the example node device 2300that the super cell 2233 a is the identified candidate super cell.

Regardless of whether the super cell 2233 a is identified as a candidatesuper cell by the processor 2350 of the example node device 2300 or bythe processor 2550 of the control device 2500 or controller 2503, theprocessor 2350 may be caused by the selection component 2345 to performcomparisons of the search criteria specified by the received instance ofthe query instructions 2710 to at least the indications of ranges ofdata values within each of the cell indexes 2330 aa and 2330 ab thatcorrespond to the two data cells 2130 aa and 2130 ab, respectively, thatare included in the super cell 2233 a. Such comparisons may be similarin nature to the those performed with super cell indexes 2430 by whichthe super cell 2233 a was identified as a candidate super cell. Sincethe super cell 2233 b is not identified as a candidate super cell,neither of the cell indexes 2330 ba and 2330 bb that correspond to thetwo data cells 2130 ba and 2130 bb, respectively, are used in such acomparison. As depicted, such comparisons lead to the data cell 2130 abbeing identified as a candidate data cell 2130 that may include one ormore data records 2133 that may meet the search criteria, while the datacell 2130 aa is not identified as a candidate data cell.

However, as previously discussed, beyond indications of data valuesbeing included in cell indexes 2330 to enable identification ofcandidate data cells in the form of field value ranges 2338, in someembodiments, the cell indexes 2330 of a data set index 2530 may eachinclude one or more other pieces of information by which eachcorresponding data cell 2130 may also be identified as a candidate datacell or ruled out as not being a candidate super cell. Among suchadditional pieces of information that have been discussed, and which maybe so included, may be unique values vectors 2335 and/or hash valuesvectors 2336.

It should be noted that, similar to the identification of candidatesuper cells, the manner in which candidate data cells are identified isalso affected by the logical operator(s) used in the search criteria. Byway of example, where the search criteria combines all of the individualsearch criteria for each of the included data fields 2134 with a logicalAND, then a data cell 2130 can only be a candidate data cell if theindividual search criteria are met for all of the data fields 2134 thatare included in the search criteria and for which there is indexinformation present in the corresponding data cell index 2330. Incontrast, and by way of another example, where the search criteriacombines all of the individual search criteria for each of the includeddata fields 2134 with a logical OR, then a data cell 2130 can be acandidate data cell if any individual search criteria is met for any ofthe data fields 2134 that are included in the search criteria and forwhich there is index information present in the corresponding cell index2330. Indeed, where the individual search criteria for each of theinclude data fields 2134 are combined in such a manner with a logicalOR, the analysis using the search criteria and the index informationwithin a cell index 2330 to determine whether the corresponding datacell 2130 is a candidate data cell can be ended once any of theindividual search criteria for any one of the include data fields 2134is determined to be met.

Turning to FIG. 20C, the cell index 2330 ab corresponding to the datacell 2130 ab of the example super cell 2233 a is depicted asadditionally including a unique values vector 2335 that includes aquantity of four unique values that were earlier identified as presentwith a particular data field 2134 among the data records 2133 within thedata cell 2130 ab. Again, the example super cells 2233 a and 2233 b aredeliberately simplified examples of super cells 2233 presented forpurposes of illustration, and it is envisioned that a real data cell2130 of a real super cell 2233 would have numerous data records 2133such that a unique values vector 2335 would likely include numerousunique values (depending on the cardinality of thereof).

With the inclusion of the depicted unique values vector 2335, theprocessor 2350 is additionally able to efficiently compare each of theunique values therein to the one or more data values or the range ofdata values specified in the instance of the query instructions 2710 forthe specific data field 2134 to which the unique values vector 2335corresponds. As depicted, the use of a vector data structure enables atleast sizable portions of the unique values vector 2335 to be loadedwithin a single cache line 2357 of a cache 2356 of the processor 2350,thereby allowing such comparisons with the unique values therein to bemade more speedily. Also, where the unique values vector 2335 includesunique values of a data type in which the unique values are all of thesame data size (e.g., all occupying a byte, a word, a doubleword, aquadword, etc.), the use of a vector data structure enables advantage tobe taken of single-instruction multiple-data (SIMD) instructions and/orother SIMD features that may be supported by the one or more processorcores 2355 of each of the processors 2300 of the node devices 2300.Through such comparisons, the processor 2350 is able to determinewhether there are any values in the corresponding data field 2134 of anyof the data records 2133 of the data cell 2130 ab that meet the searchcriteria for the corresponding data field 2134. As a result, theprocessor 2350 is able to use the unique values vector 2335 as part ofthe analysis by which the data cell 2130 ab could be ruled out as acandidate data cell, and if not so ruled out, the processor 2350 is ableto confirm whether there is at least one data record 2133 that meets thesearch criteria for at least the corresponding data field 2134. Theinclusion of unique values vectors in a cell index 2330 may become evenmore valuable for more speedily performing such searches in embodimentsin which the corresponding data cells 2130 are compressed such that thetime required to access the corresponding data cells 2130 as part ofsearching therein may be considerably increased due to the need todecompress each data cell 2130 so searched.

Indeed, if each data field 2134 that is included in the search criteriahas a corresponding unique values vector 2335, then data records 2133that meet the search criteria for all of the included data fields 2134could be identified solely from such analyses of such unique valuesvectors 2335 without accessing any of the data records 2133 within thedata cell 2130 ab, itself, which may considerably increase the speedwith which such data records 2133 may be identified. As a result, asearch bitfield 2370 may be generated that includes a bit value for eachdata record 2133 present within the data cell 2130 ab. Each of the bitsof the search bitfield 2370 may be set to a “0” or “1” value to indicatewhether its corresponding data record 2133 has been identified asmeeting the search criteria. Where the query instructions 2710 requestonly an indication of what data records 2133 meet the search criteria,then as depicted, the processor 2350 may be caused by the searchingcomponent 2346 to transmit the search bitfield 2370 to the controldevice 2500 or controller 2503, where it may be directly included in theresults data 2770 that is transmitted to the requesting device 2700 as aresponse to the query instructions 2710. It should be noted that,although bitfields 2370 are discussed herein as generated to provideindications of which data records 2133 within each data cell 2130 are orare not identified as meeting the search criteria, any of a variety ofother data structures may be used in which indications of which datarecords 2133 do and/or do not meet the search criteria may be indicatedin any of a variety of ways. By way of example a vector, linked list, orother data structure that includes the record identifiers 2132 of thedata records 2133 that meet the search criteria may be generated.

Where the query instructions 2710 request the provision of each of thedata records 2133 that meet the search criteria (or at least a requestedsubset of the data values therefrom), instead of a bitfield or otherdata structure that identifies which data records 2133 meet the searchcriteria, the processor 2350 may be caused by the searching component2346 to access the data cell 2130 ab to access each such data record2133, and then transmit each of those data records 2133 (or therequested subset of data values therefrom) to the control device 2500 orcontroller 2503 to be included in the results data 2770 transmitted tothe requesting device 2700.

Turning to FIGS. 20D, 20E and 20F, aspects of an alternate situation arepresented in which the cell index 2330 ab corresponding to the data cell2130 ab of the example super cell 2233 a does not include suchadditional information as the unique values vector 2335 depicted in FIG.20C. In this alternate situation without the benefit of having theability to search through unique values in a vector data structure thatis separate from the data cell 2130 ab, the data cell 2130 ab, afterbeing identified as a candidate data cell as discussed in regard to FIG.20B, must be searched, itself, to finally determine whether there areany data records 2133 therein that meet the search criteria.

Turning more specifically to FIG. 20D, where the search criteriaspecified by the received instance of the query instructions 2710includes data values of more than one data field 2134, and where thesearch criteria requires that the search criteria associated with all ofthose data fields 2134 be met (i.e., where a logical AND is used tocombine the search criteria associated with all of those data fields2134), the processor component 2350 may be caused by the searchingcomponent 2346 to retrieve, for each such data field 2134, the count ofunique values in the unique values index 2334 and any counts of anyduplicates thereof from any corresponding duplicate value indexes 2337that may be present in the cell index 2330 ab for the data cell 2130 ab.The processor 2350 may then analyze such counts for each of the datafields 2134 included in the search criteria to determine the relativedegrees of cardinality of the data values present within each of thosedata fields 2134. The processor 2350 may then determine the order inwhich to search each of those data fields 2134 based on the relativecardinalities of the data values present therein. More specifically, theprocessor 2350 may start such searching with the data field 2134 inwhich the data values exhibit the highest cardinality to most quicklyrule out data records 2133 of the data cell 2130 ab that don't meet thesearch criteria as part of an approach to more efficiently and speedilyrule out the data cell 2130 ab as a candidate data cell if it shouldprove to be the case that none of the data records 2133 therein meet thesearch criteria.

It should be noted that such use of counts of unique values in uniquevalues indexes 2334 and/or counts of any duplicates thereof in anycorresponding duplicate value indexes 2337 may also be used indetermining which unique values vector 2335 to use in performing asearch (as was discussed in regards to FIG. 20C) in situations wherethere are unique values vectors 2335 for more than one of the datafields 2134 that are included in the search criteria. Although the useof unique values vectors 2335 to perform a search may be considerablyfaster than directly searching data records 2133, it may still be deemeddesirable to derive an order of searches using unique values vectors2335 to even more quickly rule out data cells 2130 and/or to even morequickly narrow the quantity of data records 2133 that may still meet thesearch criteria.

Turning more specifically to FIG. 20E, as part of the approach justdescribed in regard to FIG. 20D for more quickly ruling out data records2133 that don't meet the search criteria, the processor 2350 may becaused to generate an intermediate search bitfield 2370 as each searchof data values within one of the data fields 2134 is completed. In eachsuch intermediate search bitfield 2370, a single bit may be present foreach data record 2133 within the data cell 2130 ab, and may indicatewhich ones of the data records 2133 were found to have a data value inthe corresponding data field 2134 that met the search criteria for thatdata field 2134.

Where the search criteria requires that the search criteria associatedwith all of the data fields 2134 included in the search criteria be met(i.e., where a logical AND is used to combine the search criteriaassociated with all of those data fields 2134), at least a subset ofsuch intermediate bitfields 2370 may be used to implement a progressivenarrowing of the search of the data records 2133 as each data field 2134is searched. More specifically, as each search of a data field 2134 iscompleted, resulting in the generation of a corresponding one of theintermediate search bitfields, the processor 2350 may narrow the datarecords 2133 that are searched in the search involving the next datafield 2134 to just those data records 2133 that were indicated to meetthe search criteria in the intermediate search bitfield 2370 generatedin the search involving the preceding data field 2134. In this way, eachsubsequent search involving another of the data fields 2134 includes anever smaller quantity of data records 2133 to be searched (therebybecoming ever quicker). The searches involving each different data field2134 then continue until there are no longer any data records 2134 thatmeet the search criteria for all data fields 2134 that have beensearched so far, such that there can be no data records 2133 in the datacell 2130 ab that meet all of the search criteria for all of the datafields 2134 included in the search criteria; or until there are no moredata fields 2134 for which another search is required, such that theremay be one or more data records 2133 that do meet all of the searchcriteria for all of the data fields 2134. In the latter case, wherethere is at least one of the data records 2133 that does meet theentirety of the search criteria, then a resultant search bitfieldindicating the data records 2133 that do so meet the search criteria maybe generated. The resultant search bitfield 2370, the data records 2133so identified as meeting the search criteria, and/or a subset of thedata values of each such data record 2133 may then be transmitted by theprocessor 2350 to the control device 2500 or controller 2503 forinclusion in the results data 2770 transmitted to the requesting device2700.

However, where the search criteria employs one or more other logicaloperators to combine search criteria associated with each of multipledata fields 2134 (e.g., a logical OR), then the intermediate searchbitfields 2370 may not be used to in any way narrow such searchingthrough the data fields 2134. Instead, each intermediate search bitfield2370 may be separately generated for a corresponding one of the datafields 2134 included in the search criteria, and then the bits of theresulting multiple intermediate search bitfields 2370 may be combined ina manner that employs the one or more logical operators specified in thesearch criteria for combining the search criteria of the data fields2134 included in the search criteria.

FIG. 20F depicts aspects of the performance of a search through the datarecords 2133 of the data cell 2130 ab in connection with a single datafield 2134 thereof. It should be noted that this depiction of a searchinvolving just a single data field 2134 is to provide a deliberatelysimplified example of directly searching data records 2133 for purposesof illustration. Such details as are depicted in FIG. 20F are applicableregardless of whether the search criteria includes just a single datafield 2134 such that there may be just single search through the datarecords 2133, or the search criteria includes more than one of the datafields 2134 such that there may be multiple searches through the datarecords 2133. It is envisioned that real search criteria provided in aninstance of the query instructions 2710 will often include specifieddata values and/or ranges of data values for multiple data fields 2134.

In some embodiments, each search in connection with a single data field2134 may be performed as a binary search guided by the order of recordidentifiers 2132 provided in the unique values index 2334 associatedwith the single data field 2134. In some of such embodiments, the use ofbinary searching may continue throughout the entirety of the search.However, in others of such embodiments, after binary searching isemployed to locate a first unique data value within the single datafield 2134 that meets the search criteria for the single data field2134, skip list searching, sequential forward or backward traversal, orany of a variety of other searching techniques, including the use ofregular expressions and/or other means of flexibly matching patternswithin character strings that may be applied to indexed field values offixed or variable-length character data types, may then be used tolocate one or more subsequent unique values within the single data fieldthat meet the search criteria for the single data field 2134. For anydata record 2133 that is identified as including a unique data value inthe single data field 2134 that meets the search criteria for the singledata field 2134, an indication thereof may be made in a search bitfield2370, as previously discussed. Additionally, where any such unique valueis identified, and there is a duplicate value index 2337 thatcorresponds to that unique value, indications of the one or more datarecords 2134 that include a duplicate of that unique value may also bemade in the search bitfield 2370.

Turning to FIG. 21, an alternate example is presented of a cell index2330 including additional information, beyond unique values indexes 2334and/or duplicate value indexes 2337, by which the corresponding datacell 2130 is able to be ruled out as candidate data cell. Morespecifically, the cell index 2330 generated from the example data cell2130 in FIGS. 19A-C is presented to provide an example of the use of thehash values to determine whether the data cell 2130 of FIGS. 19A-C is acandidate data cell in a search specified by an instance of the queryinstructions 2710.

As was earlier discussed in reference to FIGS. 19A-C, where the datavalues of a data field 2134 each occupy a large amount of storage spaceand/or where the data values of a data field 2134 are of variable length(e.g., text strings, audio data, video data, etc.), either generating orusing a unique values vector 2335 in which multiple ones of such datavalues are combined to form a vector data structure may be deemedimpractical. Therefore, as was also discussed, hash values that are eachof a smaller and fixed data size may be generated from each of suchunwieldy data values, and such hash values may then be combined to formhash values vector 2336. Alternatively or additionally, the highest andlowest hash values so generated may be used to define a field hash valuerange 2339.

Further, very much like the manner in which highest and lowest uniquevalues specified in field value ranges 2338 of multiple cell indexes2330 may be used to form a super cell field value range 2438 within acorresponding super cell index 2430, the highest and lowest hash valuesin field hash value ranges 2339 of multiple cell indexes 2330 may beused to form a super cell field hash value range 2439 within acorresponding super cell index 2430 (see FIG. 19C). Where the searchcriteria specified in an instance of the query instructions specifiesone or more data values (each specified explicitly as a discrete value,and not by specifying a range) for a specified data field 2134, a hashvalue may be generated of each specified data value, and the resultingone or more hash values may each be compared to a super cell field hashvalue range 2439 included in a super cell index 2430 for that specifieddata field 2134 as part of determining whether the corresponding supercell 2233 is a candidate super cell.

Returning more specifically to FIG. 21, the processor 2350 may be causedby the selection component 2345 to similarly use the depicted field hashvalue range 2339 in a comparison with one or more hash values generatedfrom one or more discrete data values specified for a specific datafield 2134 in the search criteria as part of determining whether thecorresponding data cell 2130 is ruled out as being a candidate data cellprior to the performance of any search of its data records 2133. Thismay be done alongside the processor 2350 also being caused to similarlyalso use the field value range 2338 in a comparison with the single datavalue specified in the search criteria as part of determining whetherthe corresponding data cell 2130 is so ruled out. It should be notedthat, in some embodiments where the data values of specified data field2134 are of unwieldy size and/or of variable length such that eachcomparison involving one of the data values may consume considerablymore processing and/or storage resources than each comparison involvinga hash value generated therefrom, the comparison(s) involving the hashvalues may be performed before the comparison(s) involving the uniquevalues. This may be deemed desirable to provide an opportunity for thecorresponding super cell 2130 to be ruled out, which may allow thecomparison(s) involving the unique values to be entirely avoided.

Presuming that the corresponding data cell 2130 is not ruled out as acandidate data cell through such comparisons involving ranges of values,each hash value generated from a data value specified in the searchcriteria for the specific data field 2134 may then be compared to thehash values within the hash value vector 2336 corresponding to thespecific data field 2134 to again determine whether the correspondingdata cell 2130 is ruled out as a candidate data cell. As previouslydiscussed, the hash values within the hash values vector 2336 may bearranged in either ascending or descending order by their values. Thisenables a binary search (or other type of search) to be performed withinthe hash values vector 2336 to determine whether each discrete singledata value specified in the search criteria for the specific data field2134 is present within the hash values vector 2336 as part ofdetermining whether the corresponding data cell 2130 is ruled out as acandidate data cell. As depicted, the use of a vector data structureenables at least sizable portions of the hash values vector 2336 to beloaded within a single cache line 2357 of a cache 2356 of the processor2350, thereby allowing such a search to be performed more speedily.Also, due to the hash values vector 2336 including hash values that areall of the same data size, the use of a vector data structure enablesadvantage to be taken of SIMD instructions and/or other SIMD featuresthat may be supported by the one or more processor cores 2355 of each ofthe processors 2300 of the node devices 2300.

As previously discussed, within a unique values vector 2335 the uniquevalues are organized in an order that mirrors the order in which therecord identifiers 2132 are organized within the unique values index2334, thereby correlating each of the unique values to the recordidentifier 2132 of the data record in which the unique value is present.However, as also previously discussed, the fact that the hash values areorganized in a different order according to their values (e.g., anascending or descending order by value) to enable searches of the hashvalue vector 2336 to be more efficiently performed results in the hashvalues within the hash value vector 2336 not being so correlated torecord identifiers 2132. As a result, although the hash value vector2336 may be used as another mechanism by which to rule out itscorresponding data cell 2130 as a candidate data cell 2130, the hashvalue vector 2336 is not able to be used as a mechanism to search fordata records 2133 of the corresponding data cell 2130 that meet thesearch criteria in the same manner in which a unique values vectors 2335may be used. Thus, presuming that the corresponding data cell 2130 isnot ruled out as a candidate data cell, a search will need to beperformed that entails accessing data records 2133 within thecorresponding data cell 2130, unless there is also a correspondingunique values vector 2335 that may be used to perform the search in lieuof making such direct accesses to data records 2133.

Referring briefly back to the above discussions of using unique valuesvectors 2335 (as described in regard to FIG. 20C) and hash valuesvectors 2336 (as described in regard to FIG. 21) to narrow down thecandidate data cells, in situations where both unique values vectors2335 and hash values vectors 2336 may be provided in cell indexes 2330for one or more data fields 2134 included in the search criteria, it maybe possible to forego using one or the other of the unique valuesvectors 2335 and the hash values vectors 2336 if the logical operatorused in the search criteria to combine the individual search criteriafor the different data fields 2134 is a logical OR. As will berecognized by those skilled in the art, this is because the logical ORoperator indicates that a candidate data cell 2130 may remain acandidate data cell (i.e., may not be ruled out as a candidate datacell) insofar as meeting the individual search criteria for a particulardata field 2134 as long one or the other of the unique values vector2335 and the hash values vector 2336 for that particular data field 2134fail to rule out that candidate data cell 2130. In some embodiments,where the search criteria makes such use of a logical OR, the processor2350 of a node device 2300 may be caused to take advantage of such asituation to increase the speed and/or efficiency with which a search isperformed by so foregoing the use of one or the other of unique valuesvectors 2335 or hash values vectors 2336 for one or more data fields.

Returning to FIG. 15A, as has been discussed, upon completion of asearch of the data set 2230 for data records 2133 that meet the searchcriteria specified by a received instance of the query instructions2710, the node devices 2300 may provide the control device 2500 orcontroller 2503 with either bitfields indicating which data records 2133meet the search criteria, or at least some of the data values of each ofdata records 2133 that meet the search criteria (if not the entirety ofeach of those data records 2133). Upon receiving either such bitfieldsor such data values, the processor 2550 of the control device 2500 orcontroller 2503 may be caused by the query component 2545 to combinesuch provided results of the search to form the results data 2770, andthen transmit the results data 2770 to the requesting device 2700 inresponse to the query instructions 2710.

However, in some embodiments, the query instructions 2710 may, inaddition to providing the search criteria of a search for data records2133, include executable task instructions specifying one or moreoperations to be performed with data values of any data records 2133that may be identified as meeting the specified search criteria. In suchembodiments, such specified one or more operations may be performedwithin each of the node devices 2300 in which at least one of such datarecords 2133 is identified within a super cell 2233 stored therein, andthese operations may also be performed on the same thread(s) ofexecution within each corresponding node device that performed thesearch that identified at least one of such records.

FIG. 22 depicts aspects of the performance of such operations within anode device 2300 in which at least one data record 2133 was identifiedas meeting the search criteria of a search performed therein by theprocessor 2350. In executing the control routine 2340, the processor2350 of the example node device 2300 may be caused by the executioncomponent 2347 to employ indications in a search bitfield 2370 of therebeing at least one data record 2133 that meets the search criteriawithin a data cell 2130 of a super cell 2233 to retrieve the one or moredata records 2133. The processor 2350 may then be caused by theexecution component 2347 to execute the task instructions within thequery instructions 2710 to thereby perform the one or more operationswith data values of the retrieved data records 2133. As previouslydiscussed, as a measure to improve the speed and/or efficiency ofexecuting the task instructions from the query instructions 2710,performance of the task instructions with each retrieved data record2133 or with the retrieved data records 2133 of each data cell 2130 fromwhich they may be retrieved may be executed in a separate process, withthe separate processes being distributed among multiple threads ofexecution supported by the processor 2350. As the results of theperformance of the task instructions with each such data record 2133 iscompleted, and/or as the results of the performance of the taskinstructions with the data records 2133 of each data cell 2130 iscompleted, the processor 2350 may be caused by the execution component2347 to transmit indications of the results to the control device 2500or controller 2503 to be combined with corresponding results from othernode devices 2300 to form the results data 2770 transmitted to therequesting device 2700.

Returning to FIGS. 15A and 15B, in various embodiments, each of theprocessors 2350 and 2550 may include any of a wide variety ofcommercially available processors. Further, one or more of theseprocessor components may include multiple processors, a multi-threadedprocessor, a multi-core processor (whether the multiple processor corescoexist on the same or separate dies), and/or a multi-processorarchitecture of some other variety by which multiple physically separateprocessors are linked.

However, in a specific embodiment, the processor 2350 of each of thenode devices 2300 may be selected to efficiently perform processingtasks with multiple portions of a data set or a data set index inparallel. By way of example, the processor 2350 may incorporate asingle-instruction multiple-data (SIMD) architecture, may incorporatemultiple processor cores, and/or may incorporate the ability to supportmultiple simultaneous threads of execution per processor core.

In various embodiments, each of the storages 2360 and 2560 may be basedon any of a wide variety of information storage technologies, includingvolatile technologies requiring the uninterrupted provision of electricpower, and/or including technologies entailing the use ofmachine-readable storage media that may or may not be removable. Thus,each of these storages may include any of a wide variety of types (orcombination of types) of storage device, including without limitation,read-only memory (ROM), random-access memory (RAM), dynamic RAM (DRAM),Double-Data-Rate DRAM (DDR-DRAM), synchronous DRAM (SDRAM), static RAM(SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), flash memory, polymermemory (e.g., ferroelectric polymer memory), ovonic memory, phase changeor ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS)memory, magnetic or optical cards, one or more individual ferromagneticdisk drives, non-volatile storage class memory, a plurality of storagedevices organized into one or more arrays (e.g., multiple ferromagneticdisk drives organized into a Redundant Array of Independent Disks array,or RAID array), or layered or array). redundant sets of storage devicesin which non-volatile storage devices serve to preserve the contents ofvolatile storage devices in the event of an error condition or powerfailure (e.g., storage class memory accompanying RAM). It should benoted that although each of these storages is depicted as a singleblock, one or more of these may include multiple storage devices thatmay be based on differing storage technologies. Thus, for example, oneor more of each of these depicted storages may represent a combinationof an optical drive or flash memory card reader by which programs and/ordata may be stored and conveyed on some form of machine-readable storagemedia, a ferromagnetic disk drive to store programs and/or data locallyfor a relatively extended period, and one or more volatile solid statememory devices enabling relatively quick access to programs and/or data(e.g., SRAM or DRAM). It should also be noted that each of thesestorages may be made up of multiple storage components based onidentical storage technology, but which may be maintained separately asa result of specialization in use (e.g., some DRAM devices employed as amain storage while other DRAM devices employed as a distinct framebuffer of a graphics controller).

However, in a specific embodiment, the storage 2360 of one or more ofthe node devices 2300 used to store a portion of a data set and/or of adata set index may be implemented with a redundant array of independentdiscs (RAID) of a RAID level selected to provide fault tolerance toprevent loss of one or more of these datasets and/or to provideincreased speed in accessing one or more of these datasets.

In various embodiments, the input device 2720 may be any of a variety oftypes of input device that may each employ any of a wide variety ofinput detection and/or reception technologies. Examples of such inputdevices include, and are not limited to, microphones, remote controls,stylus pens, card readers, finger print readers, virtual realityinteraction gloves, graphical input tablets, joysticks, keyboards,retina scanners, the touch input components of touch screens,trackballs, environmental sensors, and/or either cameras or cameraarrays to monitor movement of persons to accept commands and/or dataprovided by those persons via gestures and/or facial expressions. Invarious embodiments, the display 2780 may be any of a variety of typesof display device that may each employ any of a wide variety of visualpresentation technologies. Examples of such a display device includes,and is not limited to, a cathode-ray tube (CRT), an electroluminescent(EL) panel, a liquid crystal display (LCD), a gas plasma display, etc.In some embodiments, the display 2780 of the requesting device 2700 maybe a touchscreen display such that the input device 2720 may beincorporated into the display 2780. In such embodiments, the inputdevice 2720 may be a touch-sensitive component of the display 2780,respectively.

In various embodiments, the network interfaces 2390 and 2590 may employany of a wide variety of communications technologies enabling thesedevices to be coupled to other devices as has been described. Each ofthese interfaces includes circuitry providing at least some of therequisite functionality to enable such coupling. However, each of theseinterfaces may also be at least partially implemented with sequences ofinstructions executed by corresponding ones of the processor components(e.g., to implement a protocol stack or other features). Whereelectrically and/or optically conductive cabling is employed, theseinterfaces may employ timings and/or protocols conforming to any of avariety of industry standards, including without limitation, RS-232C,RS-422, USB, Ethernet (IEEE-802.3) or IEEE-1394. Where the use ofwireless transmissions is entailed, these interfaces may employ timingsand/or protocols conforming to any of a variety of industry standards,including without limitation, IEEE 802.11a, 802.11ad, 802.11ah,802.11ax, 802.11b, 802.11g, 802.16, 802.20 (commonly referred to as“Mobile Broadband Wireless Access”); Bluetooth; ZigBee; or a cellularradiotelephone service such as GSM with General Packet Radio Service(GSM/GPRS), CDMA/1×RTT, Enhanced Data Rates for Global Evolution (EDGE),Evolution Data Only/Optimized (EV-DO), Evolution For Data and Voice(EV-DV), High Speed Downlink Packet Access (HSDPA), High Speed UplinkPacket Access (HSUPA), 4G LTE, 5G, NVMe, PCIe, etc.

However, in a specific embodiment, the network interface 2390 of one ormore of the node devices 2300 may be implemented with multiplecopper-based or fiber-optic based network interface ports to provideredundant and/or parallel pathways in exchanging one or more of thesuper cells 2233 with the one or more data devices 2100.

FIGS. 23A and 23B, taken together, illustrate an example embodiment of alogic flow 3100. The logic flow 3100 may be representative of some orall of the operations executed by one or more embodiments describedherein. More specifically, the logic flow 3100 may illustrate operationsperformed by the processors 2350 and 2550 in executing the controlroutine 2340 and 2540, and/or performed by other component(s) of atleast one of the node devices 2300, the control device 2500 and/or thecontroller 2503.

At 3110, the processor of each of multiple node devices (e.g., theprocessor 2350 of each of multiple node devices 2300) may generate datarecords (e.g., the data records 2133) from data values and/or stream(s)of data values received from one or more data devices serving as a datasource (e.g., the one or more data devices 2100). At 3112, theprocessors of each of the multiple node devices may group the generateddata records into data cells (e.g., the data cells 2130). As has beendiscussed, the processor may so generate and group data records inaccordance with one or more rules specifying such parameters as whatdata fields are to be provided in each data record, the field labels forthe data fields, minimum and/or maximum quantities of the data recordsto include in each data cell, and/or minimum and/or maximum data sizesof each data cell.

At 3114, the processor of each of the multiple node devices may generatea corresponding cell index for each generated data cell (e.g., the cellindexes 2330). As has been discussed, within each node device thegeneration of each cell index therein may be performed within a separateprocess, and those separate processes may be distributed among multiplethreads of execution supported by the processor thereof.

At 3116, the processor of each of the multiple node devices may groupmultiple ones of the generated data cells within each node device intoone or more super cells (e.g., the super cells 2233). At 3118, theprocessors of each of the multiple node devices may group indications ofranges of data values from the cell indexes into one or more super cellindexes, and in a manner that corresponds to the grouping of multipledata cells into one or more super cells at 3116. As has been discussed,the processor may so group the data cells in accordance with one or morerules specifying such parameters as minimum and/or maximum quantities ofthe data cells to include in each super cell, and/or minimum and/ormaximum data sizes of each super cell.

As has been discussed, in some embodiments, a control device (e.g., thecontrol device 2500) or a controller incorporated into one of themultiple node devices (e.g., the controller 2503 incorporated into oneof the node devices 2300) may participate in responding to a query ofthe data set that may be received in the form of an instance of queryinstructions (e.g., an instance of query instructions 2710). Morespecifically, in such embodiments, the control device or controller mayperform operations to identify one or more candidate super cells thatmay include one or more data cells that may include one or more datarecords that meet search criteria specified in the instance of queryinstructions. In support of the control device or controller performingsuch a function in such embodiments, the processors of each of themultiple node devices may transmit the super cell index(es) theygenerate to the control device or controller at 3120.

At 3130, the processor of the control device or controller (e.g., theprocessor 2550) may receive an instance of query instructions from arequesting device (e.g., one of the requesting devices 2700). The queryinstructions may include search criteria for a search for data recordswithin the data set that may meet the search criteria. As has beendiscussed, such search criteria may include a specification of a singledata value, multiple discrete data values and/or a range of data valuesto be searched for within one or more specified data fields of the datarecords of the data set.

At 3132, the processor of the control device or controller may selectone or more node devices of the multiple node devices to which to relaythe received instance of query instructions. In embodiments in which thecontrol device or controller does not perform the function ofidentifying candidate super cells, but in which multiple copies of eachsuper cell are distributed among the multiple node devices, theprocessor of the control device or controller may use stored indicationsof the manner in which each super cell is distributed among the multiplenode devices and/or recurringly updated and stored indications ofcurrent status of each node device as factors in selecting the one ormore node devices to which to relay the received instance of the queryinstructions. However, in embodiments in which the control device orcontroller does perform the function of identifying candidate supercells, the processor of the control device or controller, prior toselecting node devices to which to relay the received instance of thequery instructions, may use the search criteria with super cell fieldvalue range(s) and/or super cell field hash value range(s) provided inthe super cell indexes (e.g., the super cell field value ranges 2438and/or the super cell field hash value ranges 2439 within the super cellindexes 2430) received from the multiple node devices to identify one ormore candidate super cells (if any). The processor of the control deviceor controller may then use the identification of one or more super cellsas another factor in selecting node devices to which to relay thereceived instance of query instructions.

At 3140, the processor of the control device or controller may thentransmit the received instance of the query instructions to the selectednode devices. As has been discussed, in embodiments in which the controldevice or controller that performs the function of identifying candidatesuper cells, the processor of the control device or controller may alsotransmit an indication to the selected node devices of which supercell(s) stored by each of the selected node devices has been identifiedas a candidate super cell.

At 3150, the processor of each of the selected node devices may, foreach candidate super cell, identify one or more candidate data cellsthat may include one or more data records that may meet the searchcriteria. More specifically, the processors of each of the node devicesmay use the search criteria with field value range(s) and/or field hashvalue range(s) provided in the cell indexes (e.g., the field valueranges 2338 and/or the field hash value ranges 2339 within the cellindexes 2330) corresponding to the data cells within the super cell(s)identified as candidate super cells to identify one or more candidatedata cells (if any). In embodiments in which the control device orcontroller does not perform the function of identifying candidate supercells, the processor of the each of the selected node devices may do so,prior to identifying candidate data cells. More specifically, prior toidentifying candidate data cells, the processor of each of the selectednode devices, may use the search criteria with field value range(s)and/or field hash value range(s) provided in the super cell indexeswithin each of the selected node devices to identify one or morecandidate super cells (if any).

At 3152, the processor of each of the selected node devices in which atleast one candidate data cell is identified may use unique valuesvectors (if any) and/or unique values indexes to perform search(es) fordata records within each candidate data cell that meet the searchcriteria. As has been discussed, where unique values vectors areavailable in cell indexes corresponding to candidate data cells, suchunique values vectors may be used to perform at least part of the searchfor data records that meet the search criteria.

At 3154, the processor of each of the selected node devices may executetask instructions within the received instance of the query instructionsfor performing one or more processing operations of a task with datavalues of the data records identified as meeting the search criteria, ifany such task instructions are present. At 3160, the processor of eachof the selected node devices may transmit the results of the searchand/or of the performance of the task to the control device orcontroller.

At 3170, the processor of the control device or controller may assemblethe results of the search, and/or of the performance of the task,received from each of the selected node devices into a response to theinstance of the query instructions received from the requesting device.At 3172, the processor of the control device or controller may transmitthe assembled response to the requesting device.

FIGS. 24A and 24B, taken together, illustrate an example embodiment of alogic flow 3200. The logic flow 3200 may be representative of some orall of the operations executed by one or more embodiments describedherein. More specifically, the logic flow 3200 may illustrate operationsperformed by the processor 2550 in executing the control routine 2540,and/or performed by other component(s) of the control device 2500 orcontroller 2503.

At 3210, the processor of a control device or of a controllerincorporated into a node device of multiple node devices (e.g., theprocessor 2550 of the control device 2500 or of a controller 2503incorporated into one of the node devices 2300) may recurringly receiveindications of status from each node device of the multiple nodedevices. As has been discussed, such indications of status may beindications of availability of the each of the node devices to performprocessing operations, and such indications of availability may includeindications of degree of availability of processing, storage, networkand/or other resources provided by each of the node devices. As has alsobeen discussed, the processor of the control device or controller mayrecurringly update node data (e.g., the node data 2539) with therecurringly received indications of node device status.

At 3212, the processor of the control device may use such indications ofstatus of each of the node devices of the multiple node devices toderive an assignment of super cells of a data set (e.g., the super cells2233 of the data set 2230) to each of the node devices. Stateddifferently, the processor of the control device or controller mayderive the manner in which the data set is to be distributed among themultiple node devices. As has been explained, in embodiments in which adata set is to be generated by the multiple node devices, the processorof the control device or controller may derive a distribution of whatsuper cells are to be generated within each of the node devices.However, in embodiments in which a complete data set is to be providedto the multiple node devices, the processor of the control device mayderive a distribution of what super cells are to be provided to each ofthe node devices.

At 3214, the processor of the control device or controller may storeindications of the derived per-node assignments (i.e., indications ofthe derived distribution), and may do so as another part of the nodedata. At 3216, the processor of the control device or controller maytransmit, to each node device of the multiple node devices, anindication of which super cells are assigned to it.

At 3220, the processor of the control device or controller may retrievefrom rules data (e.g., the rules data 2510) one or more rules that mayprovide either an explicit indication of which data fields within thedata records within the data cells of the super cells of the data set(e.g., which ones of the data fields 2134 within the data records 2133of the data cells 2130) are selected to be indexed. Alternatively oradditionally, the processor of the control device or controller mayretrieve one or more rules for use by the processor of the controldevice or controller in determining which of such data fields are to beselected to be indexed based on a stored history of past search criteriaused in past searches and/or various heuristic algorithms, or for use bythe processors of the node devices in doing so. In embodiments in whichthe retrieved one or more rules are for use by the processor of thecontrol device or controller in determining, itself, which of such datafields are to be selected to be indexed, then the processor may proceedto use the retrieved one or more rules to do so. At 3222, the processorof the control device or controller may then transmit an indication ofwhich ones of such data fields are so selected (regardless of whetherexplicitly specified by the one or more rules, or determined by theprocessor) to the node devices to enable the node devices to generateindexes therefor. Alternatively or additionally, the processor maytransmit one or more retrieved rules for use by the processors of thenode devices in determining which ones of such data fields are to beselected to be indexed to the multiple node devices to enable theprocessor of each node device of the multiple node devices to do so.

Again, as has been discussed, in some embodiments, the control device orcontroller may participate in responding to a query of the data set thatmay be received in the form of an instance of query instructions (e.g.,an instance of query instructions 2710). More specifically, in suchembodiments, the processor of the control device or controller mayperform operations to identify one or more candidate super cells thatmay include one or more data cells that may include one or more datarecords that meet search criteria specified in the instance of queryinstructions. At 3230, in support of the control device or controllerperforming such a function in such embodiments, following the generationof, and/or a repetition of the generation of, cell indexes and supercell indexes of a data set index (e.g., the cell indexes 2330 and supercell indexes 2430 of the data set index 2530) by the node devices, theprocessor of the control device or controller may receive a generated(or re-generated) super cell index from the multiple node devices foreach super cell of the data set. At 3232, in such embodiments, theprocessor of the control device or controller may store the super cellindexes.

At 3240, the processor of the control device or controller may receivean instance of query instructions from a requesting device (e.g., one ofthe requesting devices 2700). The query instructions may include searchcriteria for a search for data records within the data set that may meetthe search criteria. As has been discussed, such search criteria mayinclude a specification of a single data value, multiple discrete datavalues and/or a range of data values to be searched for within one ormore specified data fields of the data records of the data set.Additionally, for data fields in which the data type is a characterstring of fixed or variable length, the search criteria may furtherinclude regular expressions and/or other means of flexibly matchingpatterns within such character strings.

At 3242, the processor of the control device or controller may selectone or more node devices of the multiple node devices to which to relaythe received instance of query instructions. Again, in embodiments inwhich the control device or controller does not perform the function ofidentifying candidate super cells, but in which multiple copies of eachsuper cell are distributed among the multiple node devices, theprocessor of the control device or controller may use stored indicationsof the manner in which each super cell is distributed among the multiplenode devices and/or recurringly updated and stored indications ofcurrent status of each node device as factors in selecting the one ormore node devices to which to relay the received instance of the queryinstructions. However, in embodiments in which the control device orcontroller does perform the function of identifying candidate supercells, the processor of the control device or controller, prior toselecting node devices to which to relay the received instance of thequery instructions, may use the search criteria with field valuerange(s) and/or field hash value range(s) provided in the super cellindexes received from the multiple node devices to identify one or morecandidate super cells (if any). The processor of the control device orcontroller may then use the identification of one or more super cells asanother factor in selecting node devices to which to relay the receivedinstance of query instructions.

At 3250, In embodiments in which the control device or controller doesperform the function of identifying candidate super cells, the processorof the control device or controller may check whether any node deviceshave been selected to relay the received instance of the queryinstructions to. If no node devices have been selected (e.g., as aresult of the processor having determined that there are no candidatesuper cells), then the processor may transmit an indication to therequesting device of there being no data records available in the dataset that meet the search criteria at 3254. However, at least one nodedevice has been selected, then the processor may transmit the receivedinstance of the query instructions to the selected node devices at 3252.As has also been discussed, in embodiments in which the control deviceor controller that performs the function of identifying candidate supercells, processor of the control device or controller may also transmitan indication to the selected node devices of which super cell(s) storedby each of the selected node devices has been identified as a candidatesuper cell.

FIGS. 25A and 25B, taken together, illustrate an example embodiment of alogic flow 3300. The logic flow 3300 may be representative of some orall of the operations executed by one or more embodiments describedherein. More specifically, the logic flow 3300 may illustrate operationsperformed by the processor 2350 in executing the control routine 2340,and/or performed by other component(s) of at least one of the nodedevices 2300.

At 3310, the processor of each of multiple node devices (e.g., theprocessor 2350 of each of multiple node devices 2300) may receive, froma control device or controller incorporated into either the same nodedevice or another node device (e.g., from the control device 2500, orfrom a controller 2503 incorporated into either another node device 2300or the same node device 2300), one or more rules that may provide eitheran explicit indication of which data fields within data records withindata cells of one or more super cells stored within the node device thatform a portion of a data set (e.g., which ones of the data fields 2134within the data records 2133 of the data cells 2130 of the data set2230) are selected to be indexed. Alternatively or additionally, theprocessor of the node device may receive, from the control device orcontroller, one or more rules for use by the processor of the nodedevice in determining which of such data fields are to be selected to beindexed based on a stored history of past search criteria used in pastsearches and/or various heuristic algorithms. At 3312, in embodiments inwhich the received one or more rules are for use by the processor of thenode device, itself, in determining which of such data fields are to beselected to be indexed, then the processor may proceed to use thereceived one or more rules to do so.

At 3320, the processor of the node device may parse the selected datafields within each data cell of each super cell stored within the nodedevice to retrieve the data values of the selected data fields. As hasbeen discussed, as a measure to increase the speed and/or efficiency ofgenerating a corresponding cell index (e.g., one of the cell indexes2330) for each data cell, the parsing to retrieve data values of all ofthe selected data fields may be performed in a single read pass throughall of the data records within each data cell.

At 3322, the processor of the node device may, separately for eachselected data field within each data cell stored within the node device,use the data values retrieved from the data field across all of the datarecords within the data cell to generate a binary tree (e.g., one of theunique values ordering trees 2314). In so doing, the processor mayidentify (i.e., distinguish between) unique ones of the data values thatare encountered for the first time within the data field within the datarecords of the data cell and duplicates of such unique data values. Ashas been discussed and as will be familiar to those skilled in the art,the process of generating a binary tree provides the opportunity todetermine whether each data value with the data field from each one ofthe data records is a data value that is being encountered for the firsttime, such that it is added to the binary tree, and which such datavalue has been encountered before, such that it is already within thebinary tree. As has also been discussed, and as will also be familiar tothose skilled in the art, the process of generating a binary treeprovides the opportunity to sort the unique values in either anascending or descending order. Additionally, and as also previouslydiscussed, as each unique value is identified and added to the binarytree, that unique value may be correlated within the binary tree to arecord identifier (e.g., one of the record identifiers 2132) thatidentifies the data record within the data cell from which the uniquevalue was retrieved. Thus, upon being fully generated, the binary treeincludes unique values and not their duplicates. Instead, at 3324, foreach duplicate that is identified of one of the unique values that isalready stored in the binary tree, an indication of the duplicate valueis added to a table of all identified duplicate values (e.g., one of theduplicate values tables 2317). Within such a duplicate values table, therecord identifier of the data record from which the duplicate value isretrieved is stored and correlated to the record identifier of the datarecord in which the unique value that the duplicate value is a duplicateof. Also, within such a duplicate values table may be a count of howmany duplicates there are of each unique value.

At 3330, from each binary tree, the processor of the node device maygenerate a corresponding unique values index (e.g., a unique valuesindex 2334) within the cell index that corresponds to the data cell fromwhich the unique values within the binary tree were retrieved. In eachsuch unique values index, at least the record identifiers of the uniquevalues are arranged in an order that corresponds to the order into whichthe unique values were sorted as the unique values were added to thebinary tree. At 3332, from each binary tree, the processor of the nodedevice may also retrieve indications of the highest and lowest uniquevalues identified in the corresponding data field among the data recordswithin the corresponding data cell, and may use the highest and lowestvalues to generate an indication of the range of unique values (i.e.,one of the field value ranges 2338). As has been discussed, and as willbe familiar to those skilled in the art, the unique values and theircorresponding record identifiers within the binary tree may be retrievedin the sorted order by performing an in-order traversal of the binarytree. As has also been discussed, each unique values index may alsoinclude a count of the unique values identified within the correspondingdata field among the data records of the corresponding data cell.

It should again be noted that such use of a binary tree is but oneapproach to identifying unique values and duplicates thereof, and tosorting the unique values. Again, other embodiments are possible inwhich other approaches that may be based on other data structure(s) maybe used, including and not limited to a multi-layered skip list, etc.

At 3340, for each data cell, the processor of the node device may checkwhether any of the data fields that were selected to be indexed is of adata type for which a unique values vector (e.g., one of the uniquevalues vectors 2335) is to be generated. If so, then at 3342, theprocessor may so generate a unique values vector for each such datafield within the corresponding cell index. As has been discussed, theprocessor may retrieve, and/or may be provided by the control device orcontroller, one or more rules specifying one or more data types forwhich unique values vectors are to be generated. By way of example, itmay be deemed desirable to generate a unique values vector for theunique values of each of data fields of a data cell that are selected tobe indexed where the data type is such that each data value is ofidentical and/or relatively small data size (e.g., all are a byte, aword, a doubleword, a quadword, a single-precision floating point value,a double-precision floating point value, an eight-character text string,etc.).

At 3350, for each data cell, the processor of the node device may checkwhether any of the data fields that were selected to be indexed is of adata type for which a hash values vector (e.g., one of the hash valuesvectors 2336) is to be generated. If not, then at 3360, from eachduplicate values table, the processor of the node device may generateone or more corresponding duplicate value indexes (e.g., one or more ofthe duplicate value indexes 2337) within the cell index that correspondsto the data cell from which the unique values that are duplicated by theduplicate values were retrieved. In each such duplicate value index, atleast the record identifier of the unique value that is duplicated, theunique value itself, or another form of correspondence to the uniquevalue is included, along with the record identifier of each duplicate ofthat unique value. Alternatively or additionally, the correspondence ofeach duplicate value index to its corresponding unique value may bemaintained by any of a variety of types of pointer to the duplicatevalue index or relative positioning of the duplicate value index, suchas an offset relative to the corresponding unique value within theunique values index. As has also been discussed, each duplicate valueindex may also include a count of the duplicates of the unique value.

However, if at 3350, there are one or more data fields that wereselected to be indexed that are of a data type for which a hash valuevector is to be generated, then 3352, the processor of the node devicemay generate a hash value from each unique value identified in each suchdata field. Then, for each such data field, the hash values generatedfrom the unique values thereof may be sorted into ascending ordescending order, and unique ones of the hash values may bedistinguished by the processor from the duplicates thereof. As has beendiscussed, this may be done at 3354 by using the hash values generatedfrom the unique values of such a data field to generate a binary tree ofthe hash values (i.e., a hash binary tree). Again, it should be notedthat, as in the earlier described identification and sorting of uniquevalues, the use of a binary tree is but one approach, and otherapproaches that may involve other data structure(s) may be used in otherembodiments.

At 3356, from each hash binary tree, the processor of the node devicemay generate a corresponding hash values vector within the cell indexthat corresponds to the data cell from which the unique values wereretrieved and used to generate the hash values. Like the unique valueswithin each unique values vector, the hash values within each hash valuevector may be arranged in the order into which the hash values weresorted within the hash binary tree. At 3358, from each hash binary tree,the processor of the node device may also retrieve indications of thehighest and lowest unique hash values identified in the correspondingdata field among the data records within the corresponding data cell,and may use the highest and lowest hash values to generate an indicationof the range of unique hash values (i.e., one of the field hash valueranges 2339). The processor may then proceed to generate duplicate valueindex(es) at 3360.

At 3370, for each super cell stored within the node device, theprocessor of the node device may generate a corresponding super cellindex (e.g., one of the super cell indexes 2430) from indications ofranges of unique values defined by highest and lowest unique values(e.g., the field value ranges 2338) indicated in the cell indexescorresponding to the data cells of the corresponding super cell, andfrom any indications that may also be provided of ranges of hash valuesdefined by highest and lowest hash values (e.g., the field hash valueranges 2339) in those same cell indexes. More specifically, for eachsuper cell index, and for each data field that has been selected to beindexed, the processor may generate another binary tree (or again, someother data structure) from corresponding indications in eachcorresponding cell index of the highest and lowest unique values. Fromeach such binary tree (or other data structure), the processor may thenidentify the highest and lowest unique values found within thecorresponding data field across all data records within all data cellsof the corresponding super cell, and may use such highest and lowestvalues to generate an indication within the super cell index of therange of unique values for that data field throughout the correspondingsuper cell (i.e., one of the super cell field value ranges 2438). Wherethere are any data fields among the data fields selected to be indexedfor which field hash value ranges are provided within the cell indexesof the data cells within the corresponding super cell, the processor maysimilarly generate an indication within the super cell index of therange of unique hash values for that data field throughout thecorresponding super cell (i.e., one of the super cell field hash valueranges 2439).

FIGS. 26A, 26B, 26C, 26D, 26E and 26F, taken together, illustrate anexample embodiment of a logic flow 3400. The logic flow 3400 may berepresentative of some or all of the operations executed by one or moreembodiments described herein. More specifically, the logic flow 3400 mayillustrate operations performed by the processor 2350 in executing thecontrol routine 2340, and/or performed by other component(s) of at leastone of the node devices 2300.

At 3410, the processor of each node device of multiple node devices(e.g., the processor 2350 of each of multiple node devices 2300) mayreceive, from a control device or controller incorporated into eitherthe same node device or another node device (e.g., from the controldevice 2500, or from a controller 2503 incorporated into either anothernode device 2300 or the same node device 2300), an instance of queryinstructions that may been transmitted to the control device orcontroller from a requesting device (e.g., the an instance of the queryinstructions 2710 received from one of the requesting devices 2700). Thequery instructions may include search criteria for a search for datarecords within the data set that may meet the search criteria. As hasbeen discussed, such search criteria may include a specification of asingle data value, multiple discrete data values and/or a range of datavalues to be searched for within one or more specified data fields ofthe data records of the data set.

As has been discussed, in some embodiments, the control device orcontroller may participate in responding to a received instance of queryinstructions by performing operations to identify one or more candidatesuper cells of a data set that may include one or more data cells thatmay include one or more data records (e.g., candidate super cells of thesuper cells 2233 of the data set 2230 that may include one or more datacells 2130 that may include one or more data records 2133) that meetsearch criteria specified in the instance of query instructions. In suchembodiments, the control device or controller may provide an indicationof which super cell(s) stored within the node device were identified bythe control device or controller along with the query instructions. Insuch embodiments, following receipt of query instructions andaccompanying indication of which super cell(s) within the node devicehave been identified as candidate super cells at 3410, the processor mayproceed to performing operations to identify candidate data cells at3426.

However, in other embodiments in which the control device or controllerdoes not perform operations to identify candidate super cells, theprocessor within each of the node devices that is provided with thequery instructions may perform such operations to identify any candidatesuper cells that may be present among the one or more super cells thatmay be stored therein. More precisely, in such other embodiments at3420, and prior to performing operations to identify candidate datacells at 3426, the processor of the node device may use the searchcriteria with super cell field value range(s) and/or super cell fieldhash value range(s) provided in each of the super cell indexes thatcorresponds to a super cell stored within the node device (e.g., thesuper cell field value ranges 2438 and/or the super cell field hashvalue ranges 2439 within the super cell indexes 2430) to determinewhether the corresponding super cell is a candidate super cell. At 3422,after the super cell index(es) of all of the super cells stored withinthe node device have been so analyzed, the processor of the node devicemay check whether any of the one or more super cells stored within thenode device has been identified as a candidate super cell. If no supercell stored within the node device has been identified as a candidatesuper cell, then at 3424, the processor may transmit an indication tothe control device or controller that the node device has no datarecords available that meet the search criteria. However, if at leastone super cell stored within the node device has been identified as acandidate super cell, then the processor may proceed with performingoperations to identify candidate data cells at 3426.

At 3426, the processor of the node device may use the search criteriawith field value range(s) and/or field hash value range(s) provided ineach of the cell indexes that corresponds to a data cell within acandidate data cell stored within the node device (e.g., the field valueranges 2338 and/or the field hash value ranges 2339 within the cellindexes 2330) to determine whether the corresponding data cell is acandidate data cell. At 3428, after the cell index(es) of all of thedata cells within a candidate super cell stored within the node devicehave been so analyzed, the processor of the node device may checkwhether any of the one or more data cells within a candidate super cellstored within the node device has been identified as a candidate datacell. If no such data cell stored within the node device has beenidentified as a candidate data cell, then at 3424, the processor maytransmit an indication to the control device or controller that the nodedevice has no data records available that meet the search criteria.However, if at least one data cell stored within the node device hasbeen identified as a candidate data cell, then the processor may proceedwith performing operations to narrow down and then search for datarecords that meet the search criteria.

At 3430, the processor of the node device may check whether any of thecell indexes that correspond to one of the candidate data cells includesa hash values vector (e.g., one of the hash values vectors 2336) for anyof the data fields included in the search criteria. If so, then theprocessor may use such hash values vectors to narrow the set ofcandidate data cells at 3432. More specifically, where the searchcriteria specifies one or more discrete data values, and not a range ofdata values, as part of the search criteria for an individual data fieldfor which there hash values vectors in the cell indexes of candidatedata cells, the processor may generate hash values from each of the oneor more discrete data values, and may then compare those hash values tothe hash values within the hash values vectors to determine whether oneor more of the candidate data cells are able to be ruled out as havingany data records that meet the search criteria (i.e., ruled out ofcontinuing to be candidate data cells). In some embodiments, if thereare hash values vectors for more than one of the data fields included inthe search criteria where the search criteria for each of those datafields includes one or more discrete data values and no ranges of datavalues, then the processor may retrieve counts of the unique valuespresent within each such data field within the data records of eachcandidate data cell. As previously discussed, the unique values indexeswithin the data cells may include such counts (e.g., the unique valuesindexes 2334). The processor may use those retrieved counts to determinethe relative cardinality of the data values for each of such datafields. The processor may then perform the comparisons of hash valuesjust described in an order based on the relative cardinalities, startingwith the hash value vectors that correspond to the one of such datafields where the data values demonstrate the highest cardinality, andthen proceeding to the others of such data fields in order of decreasingcardinality. As the hash values vectors corresponding to each data fieldare used to narrow the candidate data cells, the use of hash valuesvectors corresponding to the next data field is able to be performedmore speedily and/or efficiently as a result of involving an evernarrower set of candidate data cells. In this way, the narrowing down ofthe candidate data cells may be performed more quickly.

At 3434, the processor may check whether the candidate data cells havebeen narrowed down to the point where there are no longer any candidatedata cells remaining. If no candidate data cells remain, then at 3436,the processor may transmit an indication to the control device orcontroller that the node device has no data records available that meetthe search criteria. However, if at least one candidate data cellremains among the data cells stored within the node device, then theprocessor may proceed with performing more operations to narrow down andthen search for data records that meet the search criteria.

At 3440, the processor of the node device may check whether any of thecell indexes that correspond to one of the candidate data cells includesa unique values vector (e.g., one of the unique values vectors 2335) forany of the data fields included in the search criteria. If so, then theprocessor may use such unique values vectors to narrow the set ofcandidate data cells at 3442. More specifically, the processor maycompare data values specified for each such data field within the searchcriteria to the unique values within the unique values vectors todetermine whether one or more of the candidate data cells are able to beruled out as having any data records that meet the search criteria(i.e., ruled out of continuing to be candidate data cells). In someembodiments, if there are unique values vectors for more than one of thedata fields included in the search criteria, then the processor mayretrieve counts of the unique values present within each such data fieldwithin the data records of each candidate data cell. Again, the uniquevalues indexes within the data cells may include such counts. Theprocessor may use those retrieved counts to determine the relativecardinality of the data values for each of such data fields. Theprocessor may then perform the comparisons of unique values justdescribed in an order based on the relative cardinalities of thecorresponding data fields, starting with the unique value vectors thatcorrespond to the one of such data fields where the data valuesdemonstrate the highest cardinality, and then proceeding to the othersof such data fields in order of decreasing cardinality. As the uniquevalues vectors corresponding to each data field are used to narrow thecandidate data cells, the use of unique values vectors corresponding tothe next data field is able to be performed more speedily and/orefficiently as a result of involving an ever narrower set of candidatedata cells. In this way, the narrowing down of the candidate data cellsmay be performed more quickly.

At 3444, the processor may check whether the candidate data cells havebeen narrowed down to the point where there are no longer any candidatedata cells remaining. If no candidate data cells remain, then at 3446,the processor may transmit an indication to the control device orcontroller that the node device has no data records available that meetthe search criteria. However, if at least one candidate data cellremains among the data cells stored within the node device, then theprocessor may proceed with performing operations to search for datarecords that meet the search criteria. More specifically, at 3450, theprocessor may use the same unique values vectors just used at 3442 tonarrow down the candidate data cells to then search for and identifydata records within the remaining candidate data cells that meet thesearch criteria for at least the data fields included in the searchcriteria for which there are unique values vectors. Stated differently,the processor may re-use the same unique values vectors to narrow downthe candidate data records within each of the remaining candidate datacells. The processor may then proceed with performing further operationsto search through and/or narrow down the data records that meet thesearch criteria so far (i.e., narrow down the candidate data recordsfurther). It should be noted that the narrowing down of candidaterecords can have the effect of further narrowing down candidate datacells in where there are instances in which the narrowing down of datarecords results in a candidate data cell no longer having any candidatedata records therein.

Referring briefly back to the above discussions of using unique valuesvectors to narrow down the candidate data cells at 3442 and to searchthe data records within the remaining candidate data cells (i.e.,narrowing down candidate data records) at 3450, although these twoactivities are depicted as separate and sequentially performed steps,other embodiments are possible in which both activities may be performedat least partially simultaneously. More specifically, a single read passmay be made through each unique values vector as part of using theunique values therein for both narrowing down candidate data cells andsearching data records (i.e., narrowing down candidate data records) atleast partially in parallel, and thereby avoiding repeated accesses toeach of the unique values vectors.

Referring briefly back to the above discussions of using unique valuesvectors and hash values vectors to narrow down the candidate data cells,and as previously discussed, in situations where both unique valuesvectors and hash values vectors may be provided in cell indexes for oneor more data fields included in the search criteria, it may be possibleto forego using one or the other of the unique values vectors and thehash values vectors if the logical operator used in the search criteriato combine the individual search criteria for the different data fieldsis a logical OR. As will be recognized by those skilled in the art, thisis because the logical OR operator indicates that a candidate data cellmay remain a candidate data cell (i.e., may not be ruled out as acandidate data cell) insofar as meeting the individual search criteriafor a particular data field as long one or the other of the uniquevalues vector and the hash values vector for that particular data fieldfail to rule out that candidate data cell. In some embodiments, wherethe search criteria makes such use of a logical OR, the processor of anode device may be caused to take advantage of such a situation toincrease the speed and/or efficiency with which a search is performed byso foregoing the use of one or the other of unique values vectors orhash values vectors for one or more data fields.

At 3460, the processor may check whether there remain any data fieldsincluded in the search criteria that have not yet been searched, andthat have been indexed such that there are at least unique valuesindexes present within the cell indexes. If so, then at 3461, theprocessor may check whether there is only one of such remainingunsearched data fields. If so, then at 3462, the processor may directlysearch such remaining unsearched data fields within the data recordswithin the remaining candidate data cells to identify data recordswithin the remaining candidate data cells that meet the search criteriafor such remaining unsearched data fields. In so doing, the processormay employ the indications of which data records within each of theremaining candidate data cells have unique data values within suchremaining unsearched data fields. As has been discussed, the processormay employ any of a variety of approaches to performing such searches,including and not limited to one or both of approaches based on binarysearching or skip lists. Additionally, where searching through use ofunique values vectors corresponding to one or more other data fields wasable to be performed, as described earlier, such that the candidate datarecords within each of the remaining candidate data cells have alreadybeen narrowed to at least some degree, the processor may take advantageof such narrowing to reduce the quantity of data records that aredirectly searched at 3462. And thus, the direct searching of datarecords at 3462 may serve to further narrow the candidate data records.

However, if at 3461, there is more than one data field included in thesearch criteria that have not yet been searched, and that have beenindexed such that there are at least unique values indexes presentwithin the cell indexes, then at 3463, the processor may retrieve countsof the unique values present within each such data field within the datarecords of each of the remaining candidate data cell. Again, the uniquevalues indexes within the data cells may include such counts. Theprocessor may use those retrieved counts to determine the relativecardinality of the data values for each of such data fields. At 3464,the processor may then perform searches similar to what was justdescribed at 3462 in an order based on the relative cardinalities of thecorresponding data fields, starting with the one of such data fieldswhere the data values demonstrate the highest cardinality, and thenproceeding to the others of such data fields in order of decreasingcardinality. In this way, the narrowing down of the candidate datarecords may be performed more quickly.

Regardless of whether the processor of the node device is caused toperform such searching for a single data field at 3462 or for multipledata fields at 3464, such narrowing down of candidate data records maylead to a narrowing of the candidate data cells. At 3465, the processormay check whether the candidate data cells have been narrowed down tothe point where there are no longer any candidate data cells remaining.If no candidate data cells remain, then at 3466, the processor maytransmit an indication to the control device or controller that the nodedevice has no data records available that meet the search criteria.However, if at least one candidate data cell remains among the datacells stored within the node device, then the processor may proceed withperforming more operations to search for and/or narrow down the datarecords that meet the search criteria.

At 3470, the processor may check whether there remain any data fieldsincluded in the search criteria that have not yet been searched, andthat have not been indexed such that there is no indexing informationpresent within the cell indexes. If so, then at 3472, the processor maydirectly search such remaining unsearched data fields within the datarecords within the remaining candidate data cells to identify datarecords within the remaining candidate data cells that meet the searchcriteria for such remaining unsearched data fields. Additionally, wheresearching through use of unique values vectors and/or unique valuesindexes corresponding to one or more other data fields was able to beperformed, as described earlier, such that the candidate data recordswithin each of the remaining candidate data cells have already beennarrowed to at least some degree, the processor may take advantage ofsuch narrowing to reduce the quantity of data records that are directlysearched at 3472. And thus, the direct searching of data records at 3472may serve to still further narrow the candidate data records. In thisway, the further narrowing down of the candidate data records may beperformed more quickly.

Again, such narrowing down of candidate data records may lead to anarrowing of the candidate data cells. At 3474, the processor may checkwhether the candidate data cells have been narrowed down to the pointwhere there are no longer any candidate data cells remaining. If nocandidate data cells remain, then at 3476, the processor may transmit anindication to the control device or controller that the node device hasno data records available that meet the search criteria. However, if atleast one candidate data cell remains among the data cells stored withinthe node device, then the processor may proceed with performingoperations to provide the control device or controller with anindication of results from the search within the node device.

At 3480, the processor of the node device may check whether the receivedinstance of the query instructions includes task instructions forperforming one or more processing operations of a task with data valuesof the data records that are identified in the search just performed asmeeting the search criteria. If not, then at 3482, the processor maytransmit an indication of the results of the search to the controldevice or controller. As has been discussed, where the queryinstructions request an indication of which data records meet the searchcriteria, a bitfield or other data structure indicating at least whichdata records within the node device meet the search criteria may betransmitted to the control device or controller. However, as has alsobeen discussed, where the query instructions request at least a subsetof the data values of each record that meets the search criteria, thosedata values or the entirety of each data record that meets the searchcriteria may be transmitted to the control device or controller.

However, if at 3480, the received instance of the query instructionsdoes include task instructions for performing such processingoperations, then at 3484, the processor may execute the taskinstructions to perform the operations of the task with data values ofthe data records identified in the search as meeting the searchcriteria. In so doing, the execution of the task instructions with datavalues of each such data record, or with the data records of each datacell in which at least one data record meeting the search criteria wasfound, may be performed in a separate process, and those processes maybe distributed across multiple threads of execution supported by theprocessor. Following and/or during such at least partially parallelexecution of multiple instances of the task instructions, the processormay transmit indications of the results of the performance of the taskto the control device or controller.

It should be noted that, as an alternative to the separate performancesof retrieving and analysis of counts of unique values described asoccurring at 3432, 3442 and/or 3463, the processor may instead, for eachcandidate data cell, retrieve all counts of unique values available fromall unique values indexes that may be present within each correspondingcell index 2330 for any of the data fields included in the searchcriteria, and may derive an order of the data fields based on relativecardinality, separately for each candidate data cell. Such an order foreach candidate data cell may then be relied up on when determining anorder in which to use hash values vectors for multiple data fields, whendetermining an order in which to use unique values vectors for multipledata fields, and/or when determining an order in which to proceedthrough multiple data fields while directly searching data records.

In various embodiments, the division of processing and/or storageresources among the devices, and/or the API architectures supportingcommunications among the devices, may be configured to and/or selectedto conform to any of a variety of standards for distributed processing,including without limitation, IEEE P2413, the ALLJOYN® standard, theIOTIVITY™ standard, etc. By way of example, a subset of API and/or otherarchitectural features of one or more of such standards may be employedto implement the relatively minimal degree of coordination describedherein to provide greater efficiency in parallelizing processing ofdata, while minimizing exchanges of coordinating information that maylead to undesired instances of serialization among processes. However,it should be noted that the parallelization of storage, retrieval and/orprocessing of data set portions of data set(s) are not dependent on, norconstrained by, existing API architectures and/or supportingcommunications protocols. More broadly, there is nothing in the mannerin which data set(s) may be organized in storage, transmission and/ordistribution via a network that is bound to existing API architecturesor protocols.

Some systems may use the HADOOP® framework, an open-source framework forstoring and analyzing big data in a distributed computing environment.Some systems may use cloud computing, which can enable ubiquitous,convenient, on-demand network access to a shared pool of configurablecomputing resources (e.g., networks, servers, storage, applications andservices) that can be rapidly provisioned and released with minimalmanagement effort or service provider interaction. Some grid systems maybe implemented as a multi-node HADOOP® cluster, as understood by aperson of skill in the art. The APACHE™ HADOOP® framework is anopen-source software framework for distributed computing.

Implementing some examples at least in part by using machine-learningmodels can reduce the total number of processing iterations, time,memory, electrical power, or any combination of these consumed by acomputing device when analyzing data. Some machine-learning approachesmay be more efficiently and speedily executed and processed withmachine-learning specific processors (e.g., not a generic CPU). Forexample, some of these processors can include a graphical processingunit (GPU), an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), a Tensor Processing Unit (TPU) byGoogle, and/or some other machine-learning specific processor thatimplements one or more neural networks using semiconductor (e.g.,silicon (Si), gallium arsenide (GaAs)) devices.

What has been described above includes examples of the disclosedarchitecture. It is, of course, not possible to describe everyconceivable combination of components and/or methodologies, but one ofordinary skill in the art may recognize that many further combinationsand permutations are possible. Accordingly, the novel architecture isintended to embrace all such alterations, modifications and variationsthat fall within the spirit and scope of the appended claims.

The invention claimed is:
 1. An apparatus comprising a processor of afirst node device of multiple node devices, and a storage of the firstnode device to store instructions that, when executed by the processor,cause the processor to perform operations comprising: store, at thefirst node device, a first super cell of multiple super cells into whicha data set is divided from a data file maintained by at least one datadevice, wherein: the multiple super cells are distributed among themultiple node devices; each super cell comprises multiple data cells;each data cell of the multiple data cells comprises multiple datarecords; and each data record of the multiple data records comprises aset of fields at which data values of the data set are stored; store,for each data cell within the first super cell, a cell index thatcorresponds to the data cell, wherein the cell index comprises: anindication of a range of values stored within a first data field of theset of fields among the data records within the data cell; and a firstunique values index that corresponds to the first data field, whereinfor each data value that is stored within the first data field among thedata records within the data cell, the first unique values indexincludes an identifier of a single data record within the data cell inwhich the data value is stored within the first data field; receive, atthe first node device, from a control device, and at least partially inparallel with other node devices of the multiple node devices, queryinstructions specifying search criteria of a search to be performed ofthe data set for data records that meet the specified search criteria,wherein the search criteria comprises at least one data value to besearched for within the first data field; in response to the receipt ofthe query instructions, and for each data cell within the first supercell, the processor is caused to perform operations of the specifiedsearch, the operations comprising: compare the data value to the rangeof values indicated in the corresponding cell index to determine whetherthe data cell includes at least one data record that meets the specifiedsearch criteria; and in response to a determination that the data cellincludes at least one data record that meets the specified searchcriteria, use at least the first unique values index to perform a searchof the data records of the data cell to identify one or more datarecords that meet the search criteria; and in response to identifying atleast one data record that meets the specified search criteria, theprocessor is caused to perform operations comprising: generate resultsdata indicative of the first super cell including at least one datarecord that meets the specified search criteria; and provide the resultsdata to the control device.
 2. The apparatus of claim 1, wherein: themultiple data cells of the first super cell comprises a first data celland a second data cell; the processor is caused to perform the specifiedsearch within the first data cell on a first thread of execution; andthe processor is caused to perform the specified search within thesecond data cell on a second thread of execution.
 3. The apparatus ofclaim 2, wherein the processor is caused to allocate a separateprocessor core of the processor to each of the first and second threadsof execution.
 4. The apparatus of claim 1, wherein: a cell indexcorresponding to a data cell of the first super cell comprises a firstset of duplicate value indexes, wherein for at least one data value thatis stored within the first data field of a data record identified in thefirst unique values index, a duplicate value index of the first set ofduplicate value indexes includes at least one identifier of anadditional data record within the data cell in which the data value isalso stored within the first data field; and the processor is caused, inresponse to identifying at least one data record within the data cellthat meets the specified search criteria, to perform operationscomprising: search within the first set of duplicate value indexes for aduplicate value index that identifies one or more additional datarecords of the data cell that also meet the specified search criteria;and generate the results data to be indicative of the one or moreadditional data records.
 5. The apparatus of claim 4, wherein: the cellindex comprises a second unique values index that corresponds to asecond data field of the set of fields within the data records of thedata cell; for each data value that is stored within the second datafield among the data records within the data cell, the second uniquevalues index includes an identifier of a single data record within thedata cell in which the data value is stored within the second datafield; the cell index comprises a second set of duplicate value indexes,wherein for at least one data value that is stored within the seconddata field of a data record identified in the second unique valuesindex, a duplicate value index of the second set of duplicate valueindexes includes at least one identifier of an additional data recordwithin the data cell in which the data value is also stored within thefirst data field; the first unique values index comprises a count ofidentifiers of data records included in the first unique values index;the second unique values index comprises a count of identifiers of datarecords included in the second unique values index; each duplicate valueindex within the first set of duplicate value indexes comprises a countof identifiers of data records included in the duplicate value index;each duplicate value index within the second set of duplicate valueindexes comprises a count of identifiers of data records included in theduplicate value index; the search criteria comprises at least one datavalue that to be searched for within the second data field; and theprocessor is caused to perform operations comprising: analyze the countof identifiers of data records within the first unique values index, thesecond unique values index, each duplicate value index within the firstset of duplicate value indexes and each duplicate value index within thesecond set of duplicate value indexes to determine relative degrees ofcardinality of the data values of the first data field and the seconddata field; and determine whether to begin the performance of thespecified search of the data records within the data cell with the firstunique values index or the second unique values index based on therelative degrees of cardinality of the data values of the first datafield and the second data field.
 6. The apparatus of claim 1, whereinthe processor is caused to perform operations comprising: parse thequery instructions to determine whether the query instructions includetask instructions for the performance of a task with data retrieved fromone or more data records identified as meeting the search criteria; andin response to a determination that the query instructions do includetask instructions for the performance of a task, perform operationscomprising: execute the instructions to perform the task at leastpartially in parallel with at least one other node device of themultiple node devices; and generate the results data to include resultsof the performance of the task as the indication that the super cellincludes at least one data record that meets the specified searchcriteria.
 7. The apparatus of claim 1, wherein the processor is causedto perform operations comprising: store, at the first node device, afirst super cell index corresponding to the first super cell, whereinthe first super cell index comprises an indication of a range of valuesstored within the first data field within the multiple data cells of thefirst super cell; in response to the receipt of the query instructions,compare the at least one data value of the search criteria to the rangeof values indicated in the first super cell index to determine whetherthe first super cell includes at least one data record within at leastone data cell of the first super cell that meets the specified searchcriteria; and condition the performance of the operations of thespecified search for each data cell within the first super cell on adetermination that the first super cell does include at least one datarecord within at least one data cell of the first super cell that meetsthe specified search criteria.
 8. The apparatus of claim 7, comprising acontroller, the controller comprises a controller processor and acontroller storage to store other instructions that, when executed bythe controller processor, cause the controller processor to performoperations to serve as the control device, the operations comprising:receive, at the first node device and from a second node device of themultiple node devices, a second super cell index corresponding to asecond super cell stored by the second node device, wherein the secondsuper cell index comprises an indication of a range of values storedwithin the first data field within the at least one data cell of thesecond super cell; in response to the receipt of the query instructions,compare the data value to the range of values indicated in the secondsuper cell index to determine whether the second super cell includes atleast one data record within at least one data cell of the second supercell that meets the specified search criteria; and in response to adetermination that the second super cell includes at least one datarecord that meets the specified search criteria, transmit the queryinstructions to the second node device to enable the second node deviceto perform the specified search within the at least one data cell of thesecond super cell.
 9. The apparatus of claim 1, wherein the processor iscaused to perform operations comprising: store, within each cell indexcorresponding to a data cell of the multiple data cells within the firstsuper cell, a unique values vector that comprises a single instance ofeach data value that is stored within the first data field among thedata records within the corresponding data cell, wherein the singleinstances of data values within the unique values vector within eachcell index are sorted by value; in response to the receipt of the queryinstructions, compare the at least one data value of the search criteriato at least one of the single instances of data values within the uniquevalues vector within each cell index to determine whether the firstsuper cell includes at least one data cell that meets the specifiedsearch criteria; and condition the performance of the operations of thespecified search for each data cell within the first super cell on adetermination that the first super cell does include at least one datacell that meets the specified search criteria.
 10. The apparatus ofclaim 1, wherein the processor is caused to perform operationscomprising: receive, at the first node device, the first super cell froma data file maintained by at least one data device; index, at the firstnode device, and at least partially in parallel with other node devicesof the multiple node devices, the multiple data records within each datacell of the multiple data cells by the first data field of the set offields in a single read pass through each data cell of the multiple datacells, wherein for each data record within the data cell, the processoris caused to: retrieve a data value from the first data field;determine, based on the data value retrieved from the first data field,whether the first data field of the data record stores a unique datavalue that has not yet retrieved by the processor from the first datafield of any data record of the data cell; and in response to adetermination that the first data field of the data record stores aunique data value, add an identifier of the data record to the firstunique values index, wherein identifiers of data records within thefirst unique values index are ordered into a vector of identifiers basedon an ordering of the corresponding unique data values in the first datafield that is selected to enable use of the first unique values index toperform the search of the data records of the data cell; and generate,within a super cell index corresponding to the super cell, an indicationof a range of the data values of the first data field within the datarecords of the data cell to enable use of the super cell index todetermine whether the at least one data value of the search criteria isare present within the first data field of any data record of the datacell.
 11. A computer-program product tangibly embodied in anon-transitory machine-readable storage medium, the computer-programproduct including instructions operable to cause a processor of a firstnode device of multiple node devices to perform operations comprising:store, at the first node device, a first super cell of multiple supercells into which a data set is divided from a data file maintained by atleast one data device, wherein: the multiple super cells are distributedamong the multiple node devices; each super cell comprises multiple datacells; each data cell of the multiple data cells comprises multiple datarecords; and each data record of the multiple data records comprises aset of fields at which data values of the data set are stored; store,for each data cell within the first super cell, a cell index thatcorresponds to the data cell, wherein the cell index comprises: anindication of a range of values stored within a first data field of theset of fields among the data records within the data cell; and a firstunique values index that corresponds to the first data field, whereinfor each data value that is stored within the first data field among thedata records within the data cell, the first unique values indexincludes an identifier of a single data record within the data cell inwhich the data value is stored within the first data field; receive, atthe first node device, from a control device, and at least partially inparallel with other node devices of the multiple node devices, queryinstructions specifying search criteria of a search to be performed ofthe data set for data records that meet the specified search criteria,wherein the search criteria comprises at least one data value to besearched for within the first data field; in response to the receipt ofthe query instructions, and for each data cell within the first supercell, the processor is caused to perform operations of the specifiedsearch, the operations comprising: compare the specified data value tothe range of values indicated in the corresponding cell index todetermine whether the data cell includes at least one data record thatmeets the specified search criteria; and in response to a determinationthat the data cell includes at least one data record that meets thespecified search criteria, use at least the first unique values index toperform a search of the data records of the data cell to identify one ormore data records that meet the search criteria; and in response toidentifying at least one data record that meets the specified searchcriteria, the processor is caused to perform operations comprising:generate results data indicative of the first super cell including atleast one data record that meets the specified search criteria; andprovide the results data to the control device.
 12. The computer-programproduct of claim 11, wherein: the multiple data cells of the first supercell comprises a first data cell and a second data cell; the processoris caused to perform the specified search within the first data cell ona first thread of execution; and the processor is caused to perform thespecified search within the second data cell on a second thread ofexecution.
 13. The computer-program product of claim 12, wherein theprocessor is caused to allocate a separate processor core of theprocessor to each of the first and second threads of execution.
 14. Thecomputer-program product of claim 11, wherein: a cell indexcorresponding to a data cell of the first super cell comprises a firstset of duplicate value indexes, wherein for at least one data value thatis stored within the first data field of a data record identified in thefirst unique values index, a duplicate value index of the first set ofduplicate value indexes includes at least one identifier of anadditional data record within the data cell in which the data value isalso stored within the first data field; and the processor is caused, inresponse to identifying at least one data record within the data cellthat meets the specified search criteria, to perform operationscomprising: search within the first set of duplicate value indexes for aduplicate value index that identifies one or more additional datarecords of the data cell that also meet the specified search criteria;and generate the results data to be indicative of the one or moreadditional data records.
 15. The computer-program product of claim 14,wherein: the cell index comprises a second unique values index thatcorresponds to a second data field of the set of fields within the datarecords of the data cell; for each data value that is stored within thesecond data field among the data records within the data cell, thesecond unique values index includes an identifier of a single datarecord within the data cell in which the data value is stored within thesecond data field; the cell index comprises a second set of duplicatevalue indexes, wherein for at least one data value that is stored withinthe second data field of a data record identified in the second uniquevalues index, a duplicate value index of the second set of duplicatevalue indexes includes at least one identifier of an additional datarecord within the data cell in which the data value is also storedwithin the first data field; the first unique values index comprises acount of identifiers of data records included in the first unique valuesindex; the second unique values index comprises a count of identifiersof data records included in the second unique values index; eachduplicate value index within the first set of duplicate value indexescomprises a count of identifiers of data records included in theduplicate value index; each duplicate value index within the second setof duplicate value indexes comprises a count of identifiers of datarecords included in the duplicate value index; the search criteriacomprises at least one data value that to be searched for within thesecond data field; and the processor is caused to perform operationscomprising: analyze the count of identifiers of data records within thefirst unique values index, the second unique values index, eachduplicate value index within the first set of duplicate value indexesand each duplicate value index within the second set of duplicate valueindexes to determine relative degrees of cardinality of the data valuesof the first data field and the second data field; and determine whetherto begin the performance of the specified search of the data recordswithin the data cell with the first unique values index or the secondunique values index based on the relative degrees of cardinality of thedata values of the first data field and the second data field.
 16. Thecomputer-program product of claim 11, wherein the processor is caused toperform operations comprising: parse the query instructions to determinewhether the query instructions include task instructions for theperformance of a task with data retrieved from one or more data recordsidentified as meeting the search criteria; and in response to adetermination that the query instructions do include task instructionsfor the performance of a task, perform operations comprising: executethe instructions to perform the task at least partially in parallel withat least one other node device of the multiple node devices; andgenerate the results data to include results of the performance of thetask as the indication that the super cell includes at least one datarecord that meets the specified search criteria.
 17. Thecomputer-program product of claim 11, wherein the processor is caused toperform operations comprising: store, at the first node device, a firstsuper cell index corresponding to the first super cell, wherein thefirst super cell index comprises an indication of a range of valuesstored within the first data field within the multiple data cells of thefirst super cell; in response to the receipt of the query instructions,compare the at least one data value of the search criteria to the rangeof values indicated in the first super cell index to determine whetherthe first super cell includes at least one data record within at leastone data cell of the first super cell that meets the specified searchcriteria; and condition the performance of the operations of thespecified search for each data cell within the first super cell on adetermination that the first super cell does include at least one datarecord within at least one data cell of the first super cell that meetsthe specified search criteria.
 18. The computer-program product of claim17, comprising a controller, the controller comprises a controllerprocessor and a controller storage to store other instructions that,when executed by the controller processor, cause the controllerprocessor to perform operations to serve as the control device, theoperations comprising: receive, at the first node device and from asecond node device of the multiple node devices, a second super cellindex corresponding to a second super cell stored by the second nodedevice, wherein the second super cell index comprises an indication of arange of values stored within the first data field within the at leastone data cell of the second super cell; in response to the receipt ofthe query instructions, compare the data value to the range of valuesindicated in the second super cell index to determine whether the secondsuper cell includes at least one data record within at least one datacell of the second super cell that meets the specified search criteria;and in response to a determination that the second super cell includesat least one data record that meets the specified search criteria,transmit the query instructions to the second node device to enable thesecond node device to perform the specified search within the at leastone data cell of the second super cell.
 19. The computer-program productof claim 11, wherein the processor is caused to perform operationscomprising: store, within each cell index corresponding to a data cellof the multiple data cells within the first super cell, a unique valuesvector that comprises a single instance of each data value that isstored within the first data field among the data records within thecorresponding data cell, wherein the single instances of data valueswithin the unique values vector within each cell index are sorted byvalue; in response to the receipt of the query instructions, compare theat least one data value of the search criteria to at least one of thesingle instances of data values within the unique values vector withineach cell index to determine whether the first super cell includes atleast one data cell that meets the specified search criteria; andcondition the performance of the operations of the specified search foreach data cell within the first super cell on a determination that thefirst super cell does include at least one data cell that meets thespecified search criteria.
 20. The computer-program product of claim 11,wherein the processor is caused to perform operations comprising:receive, at the first node device, the first super cell from a data filemaintained by at least one data device; index, at the first node device,and at least partially in parallel with other node devices of themultiple node devices, the multiple data records within each data cellof the multiple data cells by the first data field of the set of fieldsin a single read pass through each data cell of the multiple data cells,wherein for each data record within the data cell, the processor iscaused to: retrieve a data value from the first data field; determine,based on the data value retrieved from the first data field, whether thefirst data field of the data record stores a unique data value that hasnot yet retrieved by the processor from the first data field of any datarecord of the data cell; and in response to a determination that thefirst data field of the data record stores a unique data value, add anidentifier of the data record to the first unique values index, whereinidentifiers of data records within the first unique values index areordered into a vector of identifiers based on an ordering of thecorresponding unique data values in the first data field that isselected to enable use of the first unique values index to perform thesearch of the data records of the data cell; and generate, within asuper cell index corresponding to the super cell, an indication of arange of the data values of the first data field within the data recordsof the data cell to enable use of the super cell index to determinewhether the at least one data value of the search criteria is arepresent within the first data field of any data record of the data cell.21. A computer-implemented method comprising: storing, at a first nodedevice of multiple node devices, a first super cell of multiple supercells into which a data set is divided from a data file maintained by atleast one data device, wherein: the multiple super cells are distributedamong the multiple node devices; each super cell comprises multiple datacells; each data cell of the multiple data cells comprises multiple datarecords; and each data record of the multiple data records comprises aset of fields at which data values of the data set are stored; storing,for each data cell within the first super cell, a cell index thatcorresponds to the data cell, wherein the cell index comprises: anindication of a range of values stored within a first data field of theset of fields among the data records within the data cell; and a firstunique values index that corresponds to the first data field, whereinfor each data value that is stored within the first data field among thedata records within the data cell, the first unique values indexincludes an identifier of a single data record within the data cell inwhich the data value is stored within the first data field; receiving,at the first node device, from a control device, and at least partiallyin parallel with other node devices of the multiple node devices, queryinstructions specifying search criteria of a search to be performed ofthe data set for data records that meet the specified search criteria,wherein the search criteria comprises at least one data value to besearched for within the first data field; in response to the receipt ofthe query instructions, and for each data cell within the first supercell, performing operations of the specified search, the operationscomprising: comparing the at least one data value of the search criteriato the range of values indicated in the corresponding cell index todetermine whether the data cell includes at least one data record thatmeets the specified search criteria; and in response to a determinationthat the data cell includes at least one data record that meets thespecified search criteria, using at least the first unique values indexto perform a search of the data records of the data cell to identify oneor more data records that meet the search criteria; and in response toidentifying at least one data record that meets the specified searchcriteria, performing operations comprising: generating results dataindicative of the first super cell including at least one data recordthat meets the specified search criteria; and providing the results datato the control device.
 22. The computer-implemented method of claim 21,wherein: the multiple data cells of the first super cell comprises afirst data cell and a second data cell; and the method comprises:performing the specified search within the first data cell on a firstthread of execution of a processor of the first node device; andperforming the specified search within the second data cell on a secondthread of execution of the processor.
 23. The computer-implementedmethod of claim 22, comprising allocating a separate processor core ofthe processor to each of the first and second threads of execution. 24.The computer-implemented method of claim 21, wherein: a cell indexcorresponding to a data cell of the first super cell comprises a firstset of duplicate value indexes, wherein for at least one data value thatis stored within the first data field of a data record identified in thefirst unique values index, a duplicate value index within the first setof duplicate value indexes includes at least one identifier of anadditional data record within the data cell in which the data value isalso stored within the first data field; and the method comprises, inresponse to identifying at least one data record within the data cellthat meets the specified search criteria, performing operationscomprising: searching within the first set of duplicate value indexesfor a duplicate value index that identifies one or more additional datarecords of the data cell that also meet the specified search criteria;and generating the results data to be indicative of the one or moreadditional data records.
 25. The computer-implemented method of claim24, wherein: the cell index comprises a second unique values index thatcorresponds to a second data field of the set of fields within the datarecords of the data cell; for each data value that is stored within thesecond data field among the data records within the data cell, thesecond unique values index includes an identifier of a single datarecord within the data cell in which the data value is stored within thesecond data field; the cell index comprises a second set of duplicatevalue indexes, wherein for at least one data value that is stored withinthe second data field of a data record identified in the second uniquevalues index, a duplicate value index of the second set of duplicatevalue indexes includes at least one identifier of an additional datarecord within the data cell in which the data value is also storedwithin the first data field; the first unique values index comprises acount of identifiers of data records included in the first unique valuesindex; the second unique values index comprises a count of identifiersof data records included in the second unique values index; eachduplicate value index within the first set of duplicate value indexescomprises a count of identifiers of data records included in theduplicate value index; each duplicate value index within the second setof duplicate value indexes comprises a count of identifiers of datarecords included in the duplicate value index; the search criteriacomprises at least one data value that to be searched for within thesecond data field; and the method comprises: analyzing the count ofidentifiers of data records within the first unique values index, thesecond unique values index, each duplicate value index within the firstset of duplicate value indexes and each duplicate value index within thesecond set of duplicate value indexes to determine relative degrees ofcardinality of the data values of the first data field and the seconddata field; and determining whether to begin the performance of thespecified search of the data records within the data cell with the firstunique values index or the second unique values index based on therelative degrees of cardinality of the data values of the first datafield and the second data field.
 26. The computer-implemented method ofclaim 21, comprising: parsing the query instructions to determinewhether the query instructions include task instructions for theperformance of a task with data retrieved from one or more data recordsidentified as meeting the search criteria; and in response to adetermination that the query instructions do include task instructionsfor the performance of a task, performing operations comprising:executing, at the first node device, the instructions to perform thetask at least partially in parallel with at least one other node deviceof the multiple node devices; and generating the results data to includeresults of the performance of the task as the indication that the supercell includes at least one data record that meets the specified searchcriteria.
 27. The computer-implemented method of claim 21, comprising:storing, at the first node device, a first super cell indexcorresponding to the first super cell, wherein the first super cellindex comprises an indication of a range of values stored within thefirst data field within the multiple data cells of the first super cell;in response to the receipt of the query instructions, comparing the atleast one data value of the search criteria to the range of valuesindicated in the first super cell index to determine whether the firstsuper cell includes at least one data record within at least one datacell of the first super cell that meets the specified search criteria;and conditioning the performance of the operations of the specifiedsearch for each data cell within the first super cell on a determinationthat the first super cell does include at least one data record withinat least one data cell of the first super cell that meets the specifiedsearch criteria.
 28. The computer-implemented method of claim 27,wherein the first node device comprises a controller, the controllercomprising a controller processor and a controller storage to storeother instructions that, when executed by the controller processor,cause the controller processor to perform operations to serve as thecontrol device, the operations comprising: receiving, at the first nodedevice and from a second node device of the multiple node devices, asecond super cell index corresponding to a second super cell stored bythe second node device, wherein the second super cell index comprises anindication of a range of values stored within the first data fieldwithin the at least one data cell of the second super cell; in responseto the receipt of the query instructions, comparing the at least onedata value of the search criteria to the range of values indicated inthe second super cell index to determine whether the second super cellincludes at least one data record within at least one data cell of thesecond super cell that meets the specified search criteria; and inresponse to a determination that the second super cell includes at leastone data record that meets the specified search criteria, transmittingthe query instructions to the second node device to enable the secondnode device to perform the specified search within the at least one datacell of the second super cell.
 29. The computer-implemented method ofclaim 21, comprising: storing, within each cell index corresponding to adata cell of the multiple data cells within the first super cell, aunique values vector that comprises a single instance of each data valuethat is stored within the first data field among the data records withinthe corresponding data cell, wherein the single instances of data valueswithin the unique values vector within each cell index are sorted byvalue; in response to the receipt of the query instructions, comparingthe at least one data value of the search criteria to at least one ofthe single instances of data values within the unique values vectorwithin each cell index to determine whether the first super cellincludes at least one data cell that meets the specified searchcriteria; and conditioning the performance of the operations of thespecified search for each data cell within the first super cell on adetermination that the first super cell does include at least one datacell that meets the specified search criteria.
 30. Thecomputer-implemented method of claim 21, comprising: receiving, at thefirst node device, the first super cell from a data file maintained byat least one data device; indexing, at the first node device, and atleast partially in parallel with other node devices of the multiple nodedevices, the multiple data records within each data cell of the multipledata cells by the first data field of the set of fields in a single readpass through each data cell of the multiple data cells, wherein for eachdata record within the data cell, the method comprising: retrieving adata value from the first data field; determining, based on the datavalue retrieved from the first data field, whether the first data fieldof the data record stores a unique data value that has not yet retrievedby a processor of the first node device from the first data field of anydata record of the data cell; and in response to a determination thatthe first data field of the data record stores a unique data value,adding an identifier of the data record to the first unique valuesindex, wherein identifiers of data records within the first uniquevalues index are ordered into a vector of identifiers based on anordering of the corresponding unique data values in the first data fieldthat is selected to enable use of the first unique values index toperform the search of the data records of the data cell; and generating,within a super cell index corresponding to the super cell, an indicationof a range of the data values of the first data field within the datarecords of the data cell to enable use of the super cell index todetermine whether the at least one data value of the search criteria isare present within the first data field of any data record of the datacell.